{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision as tv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据集加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_path = \"./data\"\n",
    "# 加载训练集和测试集，将图片转换为tensor\n",
    "train_data = tv.datasets.FashionMNIST(\n",
    "    root=data_path, train=True, download=True, transform=tv.transforms.ToTensor()\n",
    ")\n",
    "test_data = tv.datasets.FashionMNIST(\n",
    "    root=data_path, train=False, download=True, transform=tv.transforms.ToTensor()\n",
    ")\n",
    "# 创建数据迭代器\n",
    "batch_size = 256\n",
    "train_iter = torch.utils.data.DataLoader(\n",
    "    train_data, batch_size=batch_size, shuffle=True, num_workers=0\n",
    ")\n",
    "test_iter = torch.utils.data.DataLoader(\n",
    "    test_data, batch_size=batch_size, shuffle=False, num_workers=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([256, 1, 28, 28])\n",
      "torch.Size([256])\n"
     ]
    }
   ],
   "source": [
    "for imgs, labs in train_iter:\n",
    "    print(imgs.shape)\n",
    "    print(labs.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可视化数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fashion_label(label):\n",
    "    text_labels = [\n",
    "        \"T-shirt/top\",\n",
    "        \"Trouser\",\n",
    "        \"Pullover\",\n",
    "        \"Dress\",\n",
    "        \"Coat\",\n",
    "        \"Sandal\",\n",
    "        \"Shirt\",\n",
    "        \"Sneaker\",\n",
    "        \"Bag\",\n",
    "        \"Ankle boot\",\n",
    "    ]\n",
    "    return text_labels[label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jk570JKnX6zI5OSkiItdff70Acv3118/avtenF154oYiIbN68WQD58Ic/vNXzm5qakpGREXnta187a/maNWtkeHh41vLTTjtNAHnHO96xzdf/UKF3T3z3u9+V9evXyx133CGXXnqpLFy4UCqVitx5551y1FFHyVFHHTVn29NOO23OPbrlPd3b/6233ioiIuvWrZMoiuTZz362WGv7633iE58QQD73uc+JiIhzTlasWCEvetGLZu3/sssuE0D+8z//U0T23H7ZGnrteW9j1vDwsDzucY8Tka1f83/9138JIBdffPGs5d/+9rdnLd+WMfItb3mLNBoNKYpiey9rt8fExIQA8rznPe8+1/31r38tgJxxxhmzlv/d3/2dAHLdddf1l7Xb7Tnbn3nmmVKtVqXb7faXHX/88XOexT0R3/nOd8QYI8YYedKTniRvf/vb5Zprrpn1PhOZv91Wr14t+++//6xlK1eunDWeiPgxKo5jedvb3tZf9ta3vlUA+e///u9Z6w0PD88a37Z27Pn6bL7xc0/CtvYVIFEUyc0339xf9pvf/EYAOe+88/rLtnyXiEz337e//e05x9/afGl3xYPdnq95zWtk2bJlsmHDhlnbv+QlL5Hh4eH+fVwUhaRpOmudzZs3y5IlS+TVr351f1lvXvXhD39Y8jyXU045RSqVilxzzTX9dW677TYxxsgHPvCBWfv77W9/K0EQzFq+tfnfnoTePb3lJ45j+fznPz9r3S3HlSzL5JGPfKQ84xnP6C/7xS9+IYC89a1vnbXu6aeffq/2xq6Gn//85wLItddeKyJ+PrjXXnvJW97yllnr3R/77LTTTpNarSYiIj/4wQ+k0WjI8ccfP2tMFpE5890vfvGLorWW//qv/5q13qc//WkB5Ic//OG9XkvvPv7Xf/3X/rI0TeWxj32sLF68uP/8bqtd87WvfU0Aef/73z/rOCeddJIopWY99w9kDNwpdPSex+2b3/wmeZ7f67qnnHIKo6Oj/X8/7WlPA+DPf/7zfR7nqKOO4tBDD71f53bVVVdtk/fKWst3vvMdnv/857P//vv3ly9btoyXvvSl/OAHP2BycnLWNq973etmeYde//rXEwQBV1111f06x52BOI551ateNWvZVVddxdKlS2flKodhyJvf/GaazSbf//7379cxKpUKURTxve99b6u0/GuvvZbx8XFOPfVUNmzY0P8YY3jiE584LzXr9a9//f06j4cSxx57LGNjY+y999685CUvoV6vc8UVV7BixYoH9Tjf/e53ybKMt771rWg9/Zi/9rWvpdFo9HNmlVK8+MUv5qqrrppFE/rSl77EihUreOpTnwrs+f2yPajX63NU0re85i9/+csMDw/zrGc9a1a7HX744dTr9X67bcsYOTIyQqvV4tprr33wL2Y3QW+MHRoaus91e+Ps3/7t385a/ra3vQ1gVt74TO/81NQUGzZs4GlPexrtdpsbbrjhAZ/37oZnPetZ/PjHP+bEE0/kN7/5Deeccw6rV69mxYoVfOMb3+ivN7PdelGmo446ij//+c9MTEzM2uehhx7af58DjI2N8YhHPGLWu/2qq67iyCOP5AlPeMKs9eZLZxn0mce29hX498/MKM2jH/1oGo3GNs2v9ttvP1avXv2gn/+uhgezPUWEr371q5xwwgmIyKx3wOrVq5mYmOCXv/wl4HPxoygCfFRw06ZNFEXB4x//+P46M5FlWT9Ce9VVV/WFhQEuv/xynHOcfPLJs465dOlSDjzwwDnv6/nmf3siPvnJT3Lttddy7bXXctFFF3HMMcdwxhlnzGKczRxXNm/ezMTEBE972tNm9UEv5eANb3jDrP3vbqJ9F198MUuWLOGYY44B/HzwlFNO4dJLL503ref+2GfXX389q1ev5pnPfCaXX375fQo9fvnLX+aQQw7h4IMPnnXP9lI2tyUtIwgCzjzzzP6/oyjizDPPZN26dfziF78Att2uueqqqzDG8OY3v3nWMd72trchIg+aqv6DaoQ3m03WrFnT/6xfvx7wxvGLXvQizj77bBYtWsTznvc8Lrzwwnl5/vvss8+sf/c6fFtyqffbb7/7db5r1qzhl7/85TYZ4evXr6fdbvOIRzxizm+HHHIIzrk5uTYHHnjgrH/X63WWLVu2S9bfXLFiRf8F0MPtt9/OgQceOMuIg2kl9dtvv/1+HSOOYz70oQ9x9dVXs2TJEp7+9KdzzjnnsGbNmv46N910E+DzbsfGxmZ9vvOd78wRRgmCgL322ut+ncdDid5L4Prrr+cPf/gDf/7zn3fIxKbXN1ver1EUsf/++8/qu1NOOYVOp9OfYDSbTa666ipe/OIX99Mm9vR+2R40m81ZxuB813zTTTcxMTHB4sWL57Rbs9nst9u2jJFveMMbOOigg3jOc57DXnvtxatf/ep58w/3ZDQaDYBtKhF3++23o7Weo/S6dOlSRkZGZj0Dv//973nBC17A8PAwjUaDsbGxPkVxS2Py4YIjjjiCyy+/nM2bN/PTn/6Ud77znUxNTXHSSSfxhz/8AYAf/vCHHHvssdRqNUZGRhgbG+vnk27Zblu+28G/32e+23vvnC0x33t30GfT2Ja+gm3rg63h/s6vdmc8WO25fv16xsfHueCCC+aM/z2jd+a78wtf+AKPfvSjSZKEhQsXMjY2xre+9a157+cPfvCDfO1rX+MrX/nKHC2Zm266CRHhwAMPnHPcP/7xj3Pe1/PN//ZEPOEJT+DYY4/l2GOP5WUvexnf+ta3OPTQQ3njG9/YF3L+5je/yZFHHkmSJCxYsICxsTHOP//8WX3Qe7ds+UzsTqri1louvfRSjjnmGG699VZuvvlmbr75Zp74xCeydu1a/t//+39zttlW+6zb7XL88cfzuMc9jssuu2yb7q2bbrqJ3//+93Pu14MOOgjYNlHE5cuXzxHD7W3fs7u21a65/fbbWb58+RyH//baP1vDg5oT/pGPfKRfDgx8bk0vof8rX/kKP/nJT7jyyiu55pprePWrX82//uu/8pOf/GRWXt3WVDllRiL81nB/lTqvvvpqkiTpe4EeznggKqdby2+fz5P21re+lRNOOIGvfe1rXHPNNbz73e/mgx/8INdddx2Pe9zj+gI8X/ziF1m6dOmc7bdUxY/jeM7DtCvjCU94wix1zplQSs17n+9ooaEjjzySfffdl8suu4yXvvSlXHnllXQ6HU455ZT+Ont6v9xf3HnnnUxMTMx66c53zc45Fi9ePK/YCdAXjtyWMXLx4sX8+te/5pprruHqq6/m6quv5sILL+SVr3zlvKIieyIajQbLly/nd7/73TZvc1/6G+Pj4xx11FE0Gg3+6Z/+iVWrVpEkCb/85S/53//7f88rCvZwQhRFHHHEERxxxBEcdNBBvOpVr+LLX/4yL3/5y3nmM5/JwQcfzEc/+lH23ntvoijiqquu4mMf+9icdnsg7/YtMeiz+bG1vuqpHe/M+dWegAfanr378OUvfzmnnXbavOv2NIouuugiTj/9dJ7//Ofz93//9yxevBhjDB/84Af72jEzsXr1ar797W9zzjnncPTRR5MkSf835xxKKa6++up5z3FLLYuHY98CaK055phjOPfcc7npppvYtGkTJ554Ik9/+tP51Kc+xbJlywjDkAsvvJBLLrnkoT7dBxXXXXcd99xzD5deeimXXnrpnN8vvvjiWewK2PbxI45jjjvuOL7+9a/z7W9/e5uU551zPOpRj+KjH/3ovL/vvffe97mP3REPqhH+yle+sk9fhbkP9pFHHsmRRx7JBz7wAS655BJe9rKXcemll3LGGWc8mKcxC/c2AfvWt77FMcccM+c859tmbGyMarXKjTfeOOe3G264Aa31nJvkpptummXgN5tN7rnnnr4I3K6OlStX8j//8z8452YZFz2q38qVK4Fpb9hM1W3Yuqdo1apVvO1tb+Ntb3sbN910E4997GP513/9Vy666KI+tWvx4sUce+yxD/Yl7dIYHR2dl9azPR63Xt/ceOONs9Insizj1ltvndO2J598Mueeey6Tk5N86UtfYt999+XII4/s//5w7pf58MUvfhHgPlkMq1at4rvf/S5PecpTtmmic19jZBRFnHDCCZxwwgk453jDG97AZz7zGd797nfvVl74B4LnPve5XHDBBfz4xz/mSU960lbXW7lyJc45brrppr73GrxQ0vj4eP8Z+d73vsfGjRu5/PLLefrTn95f79Zbb52zz11NUHNno+dAvOeee7jyyitJ05RvfOMbsyIk20Ib3BpWrlzZZ93MxJbv3fvTZw9XzOyrHYmHyzOxPe05NjbG0NAQ1tr7fG9+5StfYf/99+fyyy+f1aZbKxd15JFH8jd/8zc897nP5cUvfjFXXHFF3xm+atUqRIT99tuvHwkcYH4URQH4+flXv/pVkiThmmuumUWfvvDCC2dt03u33HrrrbOYOzfffPPOOekHARdffDGLFy/mk5/85JzfLr/8cq644go+/elPb5eDRinFxRdfzPOe9zxe/OIXc/XVV89b+WcmVq1axW9+8xue+cxnbveYcvfdd88pDfunP/0JoC8cvK12zcqVK/nud7/L1NTUrGj4luv1rnd78aCGqvbff/8+1ePYY4/lKU95CuCpClt6SnpKj/dHen570FMo3tJAzPOca6+9dl4qeq1Wm7O+MYZnP/vZfP3rX59FJ1+7di2XXHIJT33qU/tUyR4uuOCCWfmd559/PkVR8JznPOeBXdROwnHHHceaNWv40pe+1F9WFAXnnXce9Xqdo446CvA3ozGG//zP/5y1/ac+9alZ/26323S73VnLVq1axdDQUP8+WL16NY1Gg3/+53+eNze2l+KwJ2LVqlXccMMNs67xN7/5DT/84Q/v976OPfZYoiji3/7t32Y9e5/97GeZmJiYc9+fcsoppGnKF77wBb797W9z8sknz/r94dwvW+K6667jfe97H/vtt999ll47+eSTsdbyvve9b85vRVH0x5ltGSM3btw463etdT+KsqPH0V0Jb3/726nVapxxxhmsXbt2zu+33HIL5557bt/Z+fGPf3zW7z1Pe+8Z6Hn3Z7Z/lmVzxi/w74aHA9X5+uuvnzc62suzf8QjHjFvu01MTMyZsN4fHHfccfzkJz/hpz/9aX/Z+vXr5zBJ7k+f7enYlr7akZhvvrQ748FsT2MML3rRi/jqV786L3tn5ntzvnv6v//7v/nxj3+81f0fe+yxXHrppXz729/mFa94RT/y/sIXvhBjDGefffacaxGROe+ShyvyPOc73/kOURRxyCGHYIxBKTWLfXjbbbfNUebuOd+3HG/OO++8HX7ODwY6nQ6XX345z33ucznppJPmfN74xjcyNTU1RwPh/qBXLu+II47ghBNOmDWmz4eTTz6Zu+66i3//93+f93xbrdZ9HrMoCj7zmc/0/51lGZ/5zGcYGxvj8MMPB7bdrjnuuOOw1vKJT3xi1jE+9rGPoZSaZcc9kDFwp5Qo+8IXvsCnPvUpXvCCF7Bq1Sqmpqb493//dxqNxg6PClcqFQ499FC+9KUvcdBBB7FgwQIe+chHsn79eiYnJ+c1wg8//HC++93v8tGPfpTly5ez33778cQnPpH3v//9XHvttTz1qU/lDW94A0EQ8JnPfIY0TTnnnHPm7CfLMp75zGdy8sknc+ONN/KpT32Kpz71qZx44ok79JofLLzuda/jM5/5DKeffjq/+MUv2HffffnKV77CD3/4Qz7+8Y/3vUPDw8O8+MUv5rzzzkMpxapVq/jmN785J4fjT3/6U789Dj30UIIg4IorrmDt2rW85CUvATzd9Pzzz+cVr3gFf/VXf8VLXvISxsbG+Mtf/sK3vvUtnvKUp8x5KPYUvPrVr+ajH/0oq1ev5jWveQ3r1q3j05/+NIcddtgc0b/7wtjYGO985zs5++yz+eu//mtOPPHE/j14xBFHzCrJAfBXf/VXHHDAAbzrXe8iTdNZVHR4+PbL1VdfzQ033EBRFKxdu5brrruOa6+9lpUrV/KNb3xjFgVwPhx11FGceeaZfPCDH+TXv/41z372swnDkJtuuokvf/nLnHvuuZx00knbNEaeccYZbNq0iWc84xnstdde3H777Zx33nk89rGPnRXp3dOxatUqLrnkEk455RQOOeQQXvnKV/LIRz6SLMv40Y9+1C838pa3vIXTTjuNCy64oE9f/ulPf8oXvvAFnv/85/dZSk9+8pMZHR3ltNNO481vfjNKKb74xS/OOxE//PDD+dKXvsTf/u3fcsQRR1Cv1znhhBN2dhPscLzpTW+i3W7zghe8gIMPPrjftj2WzKte9SrWrl3bZ2aceeaZNJtN/v3f/53Fixdvd/T17W9/O1/84hf567/+a97ylrf0S5T1ohc93J8+29OxLX21I7G1+dLuige7Pf/lX/6F66+/nic+8Ym89rWv5dBDD2XTpk388pe/5Lvf/S6bNm0CPMPn8ssv5wUveAHHH388t956K5/+9Kc59NBD77W28vOf//x+WlKj0eAzn/kMq1at4v3vfz/vfOc7ue2223j+85/P0NAQt956K1dccQWve93r+Lu/+7sH1E67I3rvc/A5xpdccgk33XQT73jHO2g0Ghx//PF89KMf5a//+q956Utfyrp16/jkJz/JAQccMGv8Ofzww3nRi17Exz/+cTZu3NgvUdaLuu7q7JBvfOMbTE1NbdUWOfLIIxkbG+Piiy+eMxe8P6hUKnzzm9/kGc94Bs95znP4/ve/v9Wyf694xSu47LLL+Ju/+Ruuv/56nvKUp2Ct5YYbbuCyyy7jmmuu2WoqZw/Lly/nQx/6ELfddhsHHXQQX/rSl/j1r3/NBRdc0BfJ3la75oQTTuCYY47hXe96F7fddhuPecxj+M53vsPXv/513vrWt84SZHxAY+B2aarL/StR9stf/lJOPfVU2WeffSSOY1m8eLE897nPlZ///Of9dWaWXdgSbCH5v7USZWeddda8x//Rj34khx9+uERR1N/X3/3d38mhhx467/o33HCDPP3pT5dKpSLALOn5X/7yl7J69Wqp1+tSrVblmGOOkR/96Eeztu+VQ/j+978vr3vd62R0dFTq9bq87GUvk40bN95Xc+1QbK1E2WGHHTbv+mvXrpVXvepVsmjRIomiSB71qEf1S47NxPr16+VFL3qRVKtVGR0dlTPPPFN+97vfzSpRtmHDBjnrrLPk4IMPllqtJsPDw/LEJz5RLrvssjn7u/7662X16tUyPDwsSZLIqlWr5PTTT591z8wshbCrY1tKXomIXHTRRbL//vtLFEXy2Mc+Vq655prtKlHWwyc+8Qk5+OCDJQxDWbJkibz+9a+XzZs3z3vsd73rXQLIAQccsNXz29P6ZWvYsqRJFEWydOlSedazniXnnntuv4xFD/d1zRdccIEcfvjhUqlUZGhoSB71qEfJ29/+drn77rtFZNvGyK985Svy7Gc/WxYvXixRFMk+++wjZ555ptxzzz07phF2cfzpT3+S1772tbLvvvtKFEUyNDQkT3nKU+S8887rl0PJ81zOPvts2W+//SQMQ9l7773lne9855xyKT/84Q/lyCOPlEqlIsuXL++XJWKL0ovNZlNe+tKXysjIiAB7bOmrq6++Wl796lfLwQcfLPV6XaIokgMOOEDe9KY3ydq1a/vrfeMb35BHP/rRkiSJ7LvvvvKhD31IPve5z81b4ur444+fc5z5yjL+z//8jxx11FGSJImsWLFC3ve+98lnP/vZOfvc1j7b00uUbWtfbW2OtHLlyllznK2VKJuv/0Tufb60O+LBbk8RP48666yzZO+995YwDGXp0qXyzGc+Uy644IL+Os45+ed//mdZuXKlxHEsj3vc4+Sb3/zmnPt3a3PlT33qUwLI3/3d3/WXffWrX5WnPvWpUqvVpFarycEHHyxnnXWW3Hjjjf117m3+t6dgvhJlSZLIYx/7WDn//PPFOddf97Of/awceOCBEsexHHzwwXLhhRfOa3O0Wi0566yzZMGCBVKv1+X5z3++3HjjjQLIv/zLv+zsS7xfOOGEEyRJEmm1Wltd5/TTT5cwDGXDhg33yz6bby60YcMGOfTQQ2Xp0qVy0003icj8Y3+WZfKhD31IDjvsMInjWEZHR+Xwww+Xs88+WyYmJu71mnr38c9//nN50pOeJEmSyMqVK+UTn/jEnHW31a6ZmpqS//W//pcsX75cwjCUAw88UD784Q/Pul9EHtgYqEQehq5jfLmU5z73ufNGsB8oPv/5z/OqV72Kn/3sZ/fpuRlggAEGGGCAAQYYYIABdl/8+te/5nGPexwXXXTRfaaqDTAA7CQ6+q6GLMs45ZRT5uS9DjDAAAMMMMAAAwwwwAADbA2dTmeOaNnHP/5xtNazxCIHGODe8LA0wqMo2qri5AADDDDAAAMMMMAAAwwwwHw455xz+MUvfsExxxxDEAT9sqGve93r9thyWgM8+HhYGuEDDDDAAAMMMMAAAwwwwAD3F09+8pO59tpred/73kez2WSfffbhve99L+9617se6lMbYDfCwzYnfIABBhhggAEGGGCAAQYYYIABdjYe1DrhAwwwwAADDDDAAAMMMMAAAwwwwNYxMMIHGGCAAQYYYIABBhhggAEGGGAnYWCEDzDAAAMMMMAAAwwwwAADDDDATsL9EmZ7ln7xAzuaUnA/U9BNowF7LcXWYzYfUmfT6g6PX/kXbtq0iIkbFpJsVIRTUFtjiaYsyor/iIAVlBMQKIZCWktC8hp0Fyq6j+iycEGTVjeiM56guobqnYbl/9kiuOUeyHLc1BRSFA/smu8F17ovb9d2D7gfthEyOoRdtRwZqdNdCuOPhu4SgVyjOgYKhbKgM/+tBJRTIOBCwVYEF5b9LQAKk0I4qTCpwtYc+ZICW3NEYUG1mhKFlkqQs6jSohJkZL+wNP9Pl/zXdodd5/b2A2xHXyg1/ff9eBZUEqOHh1FJTLowpnngMNmCiOqSNgsfvZHKkjZpETCRJaQ2IG1HNDdVyTohOFBWoRxIIEgiiBEWDU1x8LI1LKw32dCsc8OapWxs1qne3Gbs6g1UbumALXBpBvY+2n87r2tL7JLPRK2KXrIIalWy0YDJ/StkoyHBgpTkwCnCBRlpYWh2YrIiIFqnqf02JFyvMeMd4r9swkx0cEMVimUjuHpCdxFsPlTRXaRY0pjg0XvdyeLGJJkztGxM5gIm19e5449LmNxQJ759gqEf3kF8x0R5UupeT/mBYqc+E9sCrfHXLODc3N+DAF2poIIA16hQrFiAHUrIa5rOYk1e04RNR22NJWw6lBNU4d8PNtZkjQAba0zmCKcsJnUwMYW6/R7URGvu8R6k+31bsEs+Ew9D7PBnYmv3+H3d+1qjggClNVQrsGgUqgl5PaC9LKaoBYTjKbXbpwjHM6TbxW0eR7rp/b+QagW9eBGqWiGvh3SXVShqAWbdJPFv7yRYN+XX25Zn4r6u614weCZ2Dexy74mHMQbPxK6B+9sPO1cdfYuBWQUBqpw4qSRGGnUkDpAooBiKsJHGVjTdYYONobUXjI1OsTieot2ImFhRpTUUobuazmKD6RoAtMUbfeINQwRsDNmIYBNB6jmLFjQZqzWphDEblZBnAW2VsPYJNZJVq9CFEHYcOhN0IQStAp1ZVCdHbxxHOh0kL3CdLrgdZyA+lHAB5HWFHdVIw1KtZ8RVC4WCwIBVRGKpu4xIHBpHKA4tDh1AkIAOwAGZEyyCZBqpBpBp8gQ6w5AnCm0EHTmUFmpBymjQoh6mtAIhVUL+UDfGgwWRuc4orcAEKK2wsSYf8QZBGBRU44wwsOhICOopOnKkDYteYciGIpLRLpUkJdE5lTBngW6hRHAmJKeKzQL/DFiFEnBGcKHFGaGWdFkUT1I1KUWoWVDxxkZlrMvwwRmV4ZyiELJMY63CFoY0DbFWo7uOcKLApBYRAev8Ne3GOo8SGVwtRqIAHTlMI0dHDkkMMiKQWBiCaGmKHirQjQId+smj0UIlyomMpd6wLN47pzZsiVoZ9YVTRK2UtNJmajQnTWLSEUVjTJENK4aqXQJjyZwhcwFdG5C5gMJA1MioSpuK6zL6qIzKsoK0CL2zpQgwbUuwIcO098wxqA+Rcr4+4/4yGuIIjIEgQCoxEhhsPaaoGmxVU1ShqIGtObQTigooq1FW0LlDWYWNFC5UuACUKGysEK1ReYhp1PzzWljoZqjCzjif++9UHmCArUIoJyxM31v9b2bda7ZqKBZHuKpBaYUK/DdxjDQMxApXE1hUYKoWU88wUUHQKpBUcE2DZAEK0OUkqbCGTh5SWANKZvmZ8LEMwmpIZcwRVjNU4ihGNZJYVOzIOgnZAlBKUMqhlECuUB2NyhUqK9BTKTqb+QwxeJYGGGCAhy0e0hJlqlJBLVuMqyVkYxXG9w/JRhTZsOD26dIYmsJoIQoKEiWsrDR5/Mhf2CvayKHVuzly9FbaLsIoh2Z6AHczokRWPOM+0Tl10yVROV0JaduYVAJyZ2gujEldgDlA4Al+u2YRsa4zRLcIWdes0b6njpnSJBsUi35bJ7mniW52Ye16XGueSMkegKKiaC/TpHsZKgtzFi6eorKgiziFOI04GDYdVsYbGTYdYgoaukukCipasTDUVLSiI8JmV9AVoWsDprIqqQ3oqJDNpkJXBaQuYNIlZC5gYdRi/8oGFkRN1scRTVOjTfRQN8cDw8xJxpaTDROga1VUGJItiZh6VJXuWMRwvc3ixRto1DoExhKHUxgDmQlpxm0yExBGBdWhLmFYsMC0OSBaz6huE0pAxcaEzpTHBFA4HLkusAgdpdhkDB2lqKsuDs141iIazhla0SZOc7pFwHiW0LUB7VZMa32DdjsmWZvR+N0U4boMKQpcuwM7kDWyM+BqMfmqxdiRKuGijPjgScJFKVaFZEawKkeHObVqF0KQAIrIN21sCobCLkYJi4emeNSKNYzRpG4dS4uCuhM2q4DbVIVxFVKEim7FUISKjICmSmjZmK4NmMwrpDZAQkVtRZPq0haNVW1WPLbFUNFlfTtkw4YFbGrXiW9v0/ivjZh256Fuvh2L+Rw8cQSLRlGVxBvfSQCBIa8bumMRRV1T1CAdcxR1wSUKk2pcoFCFEKQaVQguUhQVhQ0VLgQXGJQFE1dALcS0h6HZQa3ZCEVn9jkNMMCDBumP0yg1+52xRaS4WBwxddQCspUVlAZjBKVBlMGZEFGGIHYkQ23C2BG7gqToEEoBVpAiRFyAVo5QWYwSproJG8eHmeomKC1o453iIsq/8wWGqgWNhR1qlS6ZCnA6R6uArKVo7j9C1lZo7TCRRWuHnjSYOyP0lMZsbBHfuA69qV1erkwb4OAj47u5I3eAAQYY4P5gxxjh2+jVVEGAqyUUwzHdBQGtFZAtKogXdjhu1R/5q9ptAOQSYNEkKmPEtElUTqQs1UpKhGNI5ywzEVW9bYbaOtvil+kC7spHAbBonCiWh5s5Il7HYlOlKSm3F4pxl/Cbzkq+1ngMayaGaFXr1O8OCJuJp/kas93NtKvDGchqimxEkTQc1VrGUKWDoHCiEFEsjKbYq7qRhWGTisoZNS0qKmdIa5YZw5BWNJ1jrS1oiaPtIjbYOm0JabuYJB+i7WJaNsKlmpaFapAxErZZGLRIA0ugkoe6KR4YepOMrTwXSitUGKLiGNeISfeq09k7pjYM4d4B1WFFqCyJzgmUI5McVVhyCTDKEesCoxwjQZt9k40sCyapKMOIiUjU7PvTiiOTAotjo424Ka+zyYW4UDFaaaFDIRwqqC3N/OTMatqZprABajImu7NOd7KCDrrI7TlqfPoadvepk0QBdqSKXdwgWNFBHZpiljnEGcSCcw6jHKG2GC0UTuNsgHUaox2V0EfCF0QtVgytZ0W0mWGt2DtQDGtYbwNc1qJqAyzKj2uimLBVOt2QrPA09NT6jzGOuJFhlKMedhmp5IwEBe0pcHcldKaGwIGrjj/UTffQwBhUJYGhGgTaMxaMxtU0Rc1QVDRF1WGrgq0KOoci0ahMoQuFEkFrKaPgCgnAaYUon7oBIUFDQVQaQMGeO9YPsCtB8CFi5b/moWq7qiFbWaF7cA2lBWOcv5edwlqDOIgjIakWmLDAGP8daFsewSBQvj/AKKHdCsnXV+m0q36fwbQR7so0s7jSQS9sEVUyxDqCXOGsJUsj0nqFThphAkcYF5jAoTcaApWgNxnvY4jmmXKKTFPTt2S7DDDAAAPswdgxRvh8g6j2dCk9MowsH8PWY9rDEa1lAXldkQ1DtiwjGU5ZONSiqjMcmkwCcjE40bjSmImURStH4hKMctRcRldaVFUTgFBBb7rUozHbGae0yUVstHXaLva/lZHzcVvlDhvTljYtCVhva3QlBGBh0sI6zV2LQsYPrNIdrRI2K9SX1ggnM0wzhTvXYCcnH/TmfMigAA2ioUDTyiNI/Utca0EpoW0jJooKWgltVZC5gFgVjCtF22iqStEVx4QrSLF0JWTSVkgloOUiWjam4yLaRUSnCOnakNxolAiBshij0EkMtYqnPGf5/c4f2yVhNEQRGE04oqjtmxEN58RjBcXigGjIUqlkOKXp2pAMQ6YMRgm5aNo2onAGrYSuCtAIOhduKJayzjSIlKJhAiKtmWkdWxFycVgRJiXgriJhSgLaLmLCVmhLSOg0IopAWVIXYEWjgDAoaNQ6hNoSpTnRARbVAGkH2HU1bNvNpRzu6pjRDzRi3CKwiwuyIaFtQ7I2WFFkzmDRaOXItW935xSZC7BOYbUG69tso61xZzBKagNqCsaNoqZh3BpuL0LGncGJohCDQzFVJGxKa0wVCZkztPOIzPpj9I4lmSLOLFO6wvpOncwGfoJc16T7VBEUum0JN6SYzm7S9tuDmf1VS7CVEBJTGiuCcg4ciNHYCFwIGEFpQQGmEEwGOhOCtkXnDhtpnDEgGm39c6SsYHL/4DijUHEAjapPHSkK6Mygpg9w/6F6/xN2lvdOQoNUYggN5BbVTn0fzkxrsBbS/L41MHYoeky+2VFhWzHki2JcxZDvnaDqijBwvvmcwjrvGBfBO8qtJssDxCmc0WgRcmPRSgi0dygWhaHoRkih6IwHmNszqhOWYBjivR2mDoXTpIV3NgbGkdqAZhaTWUM7i8hsQJoFONF9f7OIwlnlZ5jDBco4nBOyfSrYCuhOQTCeort29mUP7O8BBhjgYYSdQ0fXBl1JUMZg91/OXc8Yor2XRWoFY4s3sLTSph6mLE2maAQdYl0wZLq0XEwuhrb131tDqCxVk/poocoZMh0SlZOXhl4vkg7gxEegpmxCKrMvv2kTNtk6ofIvBoM39iyKRzfuolsPmVyQcPfew7SLiDVTQ9x69zBmqk79jiGWXyuwhxnhLhBcJHQIWN+usVliQmOpRDmBsYiDSC1kU1HHKEekLEY5DBAp5Z0hyqFUDjgcigKNE03HhWzM6nRcSKcI2ZxW6diQYd1G40hUThwrzGgDvXgU6aTI+ARsj6DMroYoQi0YhkpCdb82ez9zI4392kwFCdVKSjNI0FrItGZTVvXaNeKjzU40eWnIifQmX3C7LOD3xTKMEwItxKH/RhSuR0cXKKxfv0CRKkWBz8kPQoc2oJUj0r4fpZwQKoRKkrF86SbEKtwY2BUK14VsQ0R24yjZxtBTDv80g3K4q2NGP8jeAcVBkO+dkmlo5jVkPaDBaQHNdA6lKiNEZR+oMrakgImowlQWU49TAiBWYBRkomk5TV5u5/tT0S0CJlNPQXeiya3xkSemU0QDJ9yTLyBwQqY0LRNiQotdEjL51EWopiP+S5vhH67H3LkHU9N7/ZUkuFpEsaCCq0bozBI0C5/nXQ2wsfEU9IogoaCMQ6MJuo6wqTHdgnBziukW2EqAkhibBOhCMG2LLnx/i9HYWKNUgtaLULmFqTb67g3Q3IPbeUdhJiuozwzaSprO1rbt4X5GTKUS4/ZahDSqqMk2+o71qKnO7LSGdhc2jkOn+xBEZNUM+3suLTtfFDP5pDHSFVXMqBAsFcIkoygM3W6ILQwg/Zo3uRia7RgNmMDSikIC40iCguGkQxII3VbMxF0N0qkIdXeX5OebqdzTpXIwjC6FZCF0ioiNaZWOjVAIk3nMZB5TFIZ2N6IoSqei06AFAazVOKcIIoF9Ui+AuBSy4UXIBIT3tKj/ah3RmnbZrwo/ijLIDx9ggAEeNtgpRrjSytO2o5BsNKa1b8G+q9aypDrFE4ZvY69oIwbBeA4gXRcybqt0JSQX08/dtmhSF+DEv6mcaBwKjRBoi8FRMTmLwqm+ET5hK3RdWK6v+sZ4799bYqKoAD3D3lNyY52zOJwkUoU39Bs5Rjlu7C7jutojWDM1RMuN4Grxjm7KnYtZkXBFu4hQqSEKLMoIkVJ0nDBhEzICFIJWUgq9TCNQlkQXBKVzo/d7x4a0bUTH+Qh4xwZ0i4DczoyEg04iVNUboqL3kKp6RkMlQdWqhItyGgdmLDikRVBY2mmELhyFM3RtQGH9Pd8zvN0M48+KIis8UyQvDJ1uSFEYjHaEkUUbV0ZHvOHtnI+QOFEo5WmMSkEc5gzTJZECpYRMO7Tyz2SkC4wSwsASRSlGOfIhQ2tBTO40ak2AbVXJwwqIINFuRNud0Q80QBYIdqzAZoa0FWG7BmUEFTgw/r5VSqaDeL0xRECcgl7UPFQkbnaOvHeglH0B/e+8MHRSL4gk5X4E1d+niCoFjgwUGhVZ9FCOChyuZsj3jpBCgxNsZTdq++2B0ZAkqHoVKgGuEmKT6VxSnTuUc4gRbASEgAGlQYsX2TSZYFJH0C7QHc+VMmkERnyEvOvQucOFGlsBMQriANHGR9tFBtT07cEMA1z1jHBKE/y+xLm2NMB7y+6PsRYapFHFLRhCCxCW058ZaQ0KkHGzfft/oJjFDph7XFcxpCuqdA4cIkoKwnqHMMj9uOMUttAl40O8MewU1gYgYKzGojBl5LwuXkWnyA2tiYT2pirxGkvjzxnxHU1qDRhxiloFmkVBlwApFIXVdIqQwmpv/KfeCKccE1X5bnfO97ELrBe0DCwSh+S2SjHiK3a4OJh97TMIAAMMMMAADwfsFCNcjwxTHLQX2UjExP4h0WiTsUqT4bCDRTFlveHbM8IzCfoGeF7SYXt52+CNuP7fCFo5DK6/vG1jnNZ+ezGzjO/5DO+toSfqVgbEyZXB4OiqEKMcVjQLkxZKCTcOD5E3YuJGwwtVddPdXjVdacHElqBSYKKCKM4IIktkCuphRmQKKkFOzWQkpvDq6NpiEArRpNKjMvs+0spPEIxyKASrNbEp/DvXQDUI0EBiinI/DkLIGwHpaIjSBr1J7eDiTDsA80wwJQG3TGCRkC5TNKOIoKgwVcS0Smq+E03hDFYUDtUPjojMMOZkeuailM9xNbqXy+cnQ9PCOqpXNMDPecr1tfaOE8FTrzVgncYpwe8+QCuhcA6rVf/et2UkV0WWaGHq75c8w1QUBEF5om6Xjmq4WFEs1riFmnwR5IHC5j6SIzPHir79ME1tFuXnnlKyDHpX6ZwiywK/ggBlPiV40UiZYcALZVvnBuf8eOONcH8A6W0rvSiRZzZIrkH8sxAYB8YR1BSyqEoxqX1aQDP1kds9CUohgfbUYq3QhSCpQ2cOZf39pqygM3w5xKigljSJ6hlJ4Viw0pEMC922Znw4pNs2OKMpEp8XHjgH1qJSiy4U4izKeINRaT0dwTXGf7zXZGA4bCf6BvjM73lXlLmG+FbWl9Ag1dh/G4WNQAy4kSp2qcGNQFSFKNKELYOqAotyVLWLW5dTTAiuraej9Dujb7eUDelRs42GKARtMLWIpGZR1QwdOJxAlhsK6w1qpaU0hmeccG/scAqX+4omhQSkkR/TU/HikDYRbN1QLK6iLeQLHXmYk4nzdPMiIM0DCqvJc4N12ueez5lPTacYiIBYRZ4bf3yncZGDagGJ8zmD0/z12ffCIBo+wAADzAelvIaEVkhgcJUQCe/HnGcnlhfdFuwUI9ztvZQ7jq2R7p9SGx7n6ctv54DqOqxo2jb2RvNWTKvcmb4xPRM+4jq3sXMxbC6q08ee8ZLoGeI9mvmWEdst92Pd9PphKfo2U4XdKMdjGneilWPt1BCtZSPEey1Ft7uwbgOuvZtQcrcCFTrCoQxZ0CEJCxrVDnFYEOmCRtQl0gWxKRguUwhCZalqnxbQdhEb8iE6LiJQPv/MlM6SqBQSM8rhRJHogNTkaIQ0DBiJO1R1TqwKdAW6yyOm8grBmoJknSHcHRn/WzzsboGQPd7hDrCYIcOa6hBT3ZB2EbExrXnWQVkmRiEzxPBKRkdpVPeeG6X8/RwEFl0a4L28vN4ESBx94V2UlJUHLMb4vpCSUqgUFEwfWykpAxV+G91bVv5u6gVDB05ArpGkwP5B4zZUEFsg21Jj/CFEPqyYeqQh3S/wBnmscG3l7So3u0ZPzwDvsQd6beydHKDKtICiMLRaCW0E5RQq85EqlBc79JNkNT1RVeCUoq90MDO6XhryylF2MuAU0vJDt4ktUSPFRBazWGEPWkR3SGE2tYhuWYfZvHuPQVtCtEJiA5UAUWC6DtN1qNyi0wJVOHTqCFoQxrCg0mVVYw2jiyepL8pYtvcUNZtxx9QoP1yzH5uao6iOQW/UqLYGWxClKWYyQ5Tq60VJHGCHKrjY12NWYYiOI8Q6JM+9IT7A9mNbJkPbOGGSakyxcgw3XMVWFN1RhU2UF+xbbHA1x5BRjEQhdROjjELFLTBd8hst7XWQj4fgHFIUO6lvFUp5J4/ClcatQBSiRkdQlZhosaKxKMctmiSzhlYa0c3C/ntAGdfb1YzG8B9nNUXu9UFUrJkKLKkEpC4krSgKcSgb0Tl0jGK5g5VdWpVxpOjSzGMmuwnNLMZZTZ4ZXGn4TzOBVJmiU55A2VWFGFyh/dgpoIYs1C1sLFAxsxXRZxrgvQF2T9B/GWD3g1KePtV79ncBY+1hhXtzwmntdaJMgG3E5Csa2KEYs7lNeMs6zPhW5jxl2gvas3Rke4NEDzAtakvsFCPc1iO6+2Q88cBbWRC1Oai6hgWmSdvF3JOP0Cx6Amk+2q2VECrbj2z3otlbM5p7UXDwBkrqwj5NXat5BnGlMbj7jIr3ae9KkVsz57fRsM2ycDMjps3iepMN9VFsPcaI+CjJbo5+JDwpCMOcSpxRCXJiXVALM2+Eq4Kqmf57yPgSZdoKE7ZKV0qKOv5j+iVRHKIUkS76hmEW5GgnJCb3kXDlUIFQ1A3pSIg0DXGw28XB54UkYJcJdj8hDRTNMMIWPj+4XUR0bdAX0Ond29ORbB+B9tHx6fboRbZRrqSdq34k3Nkyd1mJp1f313eY8hje0PeTIqUUcx43JWjnje9plXAwkSNMcrQS7DroVhQSBIAg89FIdyG4GNLFis7e2hu3hULyGRPLHsrx27cbaO1K1eAZv5cQp8gKTwNVVqFSDYU3oCUQpMzT77evEiQQ5vVDlo6AXkldVDkvKLSvzhBYAuMI4gKqIW5hFbGehSDh7j8GzYGmHwlXztPPcYLKt4iE54JJfUrAoqTJktoEDdNhRbiJhu6QTjl0ZW+6Uxo9rglTjbYKFwgUFpUVZf/4qJ5zYGvTRoLSGkyAokCKXfsef7hBQoMbruIWNShqkC3WFDWFqziKBQUuESpVMIs0cdU/I4ocRQ5NQVcFpbWfpO1M9AaXmQOBNqhKDLUqpmaJaxlULJKFuG5MXpj+tkozO3AvPdcpnq6e+zGj0EKaBzjjgw1FADYWdN2Qj9WgAvkCQxa2CVxB5gxZGQkXq7C5N8L79POZNPJeBL8cO8VBId7418ZhYp8i1YuEK9Vj/cg026FMV/D2/CAivsdiBgtipx1vvmPNt1xpX/HFlYb4/dl2e5YPMI37qCaEVv7dGwZQiXEL6tjRCggEW0uF7O+TMhVKe2fn9p7blsseQJ/uMCNcBYGvAx4EpI0AU81ZELWpmZRcDFOuQupCrOhp41ocpozo9couaWRaKK004OY1rGdgPhE3s0Vz9/bjepRzKMsGlfm1Yui6cN59zecQqAQ53QWK1t5Vko0h0aYJmJraprbaVWGUIzIFQZiTBAWJLoj7H2+Mx6qgojMS7enohWichGSl6J3um4w+kmhKB0uoLGiIpPDGhBKqRhMqS8X4Ulya0hlpQAL/vftx0WdAawgClNGYJKASWiTMCLX1ucFOe/p+mVffIzj3hL+0mo7AGiWl2Bp9qrITRahtn6pelJRqcQoXeKEcrQVTKuTOFNjzhrXvI7/baXr1TLp1T0ysZ7CLk34eolHOC2KNRuRLDLRTtM1Qu3D5cJ1BtEkhaxQSCrYuSDSdO98fWwvt6+tqweEjst44U9O0cVv+PZOCLvhccgX0xN20+BQXR3+iTMlSmINyH0p665d/a0G08g7CQiNpgG4J0YY2ei2YTa09h4pe6olgNFQrSBQggWcO2FgjypcfM4GgCo2LDdoqTCpUCstC1WFJ0KSqM2oqJ1KOUDlM6bRSBlzk+72oaIrhsE9WcAbf55WAYqHCVUDXNEZV0E2N6hYEmw26axFrIS8G0butYUZajsxcNl/+c2ywjQSJDaoQTFfQVnBBGdUOFFFUUK93CKMCK5quCyjEUB0VqvtvJhxp0olDNjYSOnFAFmhaxjv6XceQ3xWTarzTpZuirCW/SXCTILYXhd15E2YRKaPg08d0oaIYCpCRENVQ6CDvDxO9VCJmnqWaYROXRrmizJspxyEHFNZAVn5rQQcOEmDYITEUNaFVROQtoV3mgPePo/G6CCWjavZF3MsFlka7UgKR9kyFhQayHNXsovJd+EUxwIOPrVRR0pXEG1jA9CQH/x7QCslypNPxLJUHerytLRc3bYDf3223Z/kAs7GlYTujKoqrhuSLElw1wA7FpAs0tg5h1xAsqKFcOSz15k6FRXUzKKx/98wzzs49fv9/236+24kdZ4RXKqjlS3D1mObSgEUjmzmouqavdj5RUsad+BrIvvyPbxRPa84IdcGQ7rI0nGBIdwiVpaYywtII7xnWtqTq9kqNmVIgbOayHnq/hfhJWC/f1qLoimHKJeQSMG6r3J2PMmUTHF4Uq7+PcpcawaHJJWB5dYLfrMooaiH1O2KW3D0Ea9ftqObdKQi0I4pTdOKj08NRh8R4A3wo6PYN8AVBi4rO6Lpe+TFvhAuKQLmSji6e1q8Kqjol0TmR81S6VFmc1iQmx4liQdDu18TW2iGxUCQQROXEYndFEKAbdVQUEQ07knpOUEmxokmdoV1EpRFOaZj3KOfeQI7K+tTTTo1egHZ6Imad7m9X9PPAlV8uCqO9Y8WUCuj1MO07AVy5NycKW4oeioArGSqF03RsSOG8cZpJL4okmMA7zoqhkO6qYdpxhWD9FEmnQ9DOHqIGv28ETUXtZkUyqbBjQn5Ijh11FIUm64ZY60uPkWrEKsSIz2vUPR4504Z3L2+7pKbjFBhBojJHH/oRdayCTE+/KOxWBnGZ/la2NOy1V/xGC4VSuE6EyoR4fYfKnzYS/7nj6dnN3byKQI+WGk8rokstxNUiXKSxMWR1n/NrMiGcCjGZlLniinjSMdJJOUBv5sB4HQqLVgUKR1UXRMazCFzosFUQJ6AC2nmNcNRhIyiq+FJniaJYAC4B3Y0wm0dRXUe8MWfoT23MphzVzXCTk5Duuvf7DsW2RARkxg0tW1/fNhI6hy2mWFQjbFoqazNMy+GGDO1lIUXdMLpgksUH3M3ogg7NImJdmtCyMQurbQ4Zu4VF1TYbXZ0/ucVskhrNNCZvNsiymGJzSOv2BjLhUM02ak0KzQxpCXYNSF6eo9tJk+aSGjnLOQHYiqa9V0SxtEKwJCVJMgKxnlUZOAKx9CtklCkvvfQhKX19fswA0V67wCnodkNUVk7/AsGEBToRZEiwTmhraKd1WF/HAbnSntWk8LT3OZRMtUX6DtNpN2WIXJUpUMY4VD3E7rOA3IAabxHcvh414Y0qVVLj1Myo/gAPC+hKgl4yhqsn5Xu0pAtrjYsCMAo91YW71yI7MsjlBQ123P4HmB8lG6jPhOlRe6IItXAEkoRizDBxaEy6yCDaIFEABsJKjM7GYOEoSgRVeGehmvTVTNRU6bSZZ5ydPv6MKHz5tyo9m1ulr/fW3U5DfIdGwl09Jh9OKGqK0ThlgWky5SpMFFWaNsbgfL1KXD9PG7wRHs7IMR4zk4zolERZhrQiRKGVwpQGtkXIxZKLYNT07+BrIjtgpu0WKk1IgFHK103G4sTRlYJxldOWAI0ra4l7y2+mGNyWsChqJqU62qHtFN1WiCThjmranQathNgUmEB549vMiICXUfBE51R0RlWn3lBD03UhRam+2vN863Ji0GM2RKro09HFKZwqJxQoEp2VkXBvaPYi4c7MZgjvblBGo6IIlSSYJKcSZURhTmoDus5XAPDTFcF4sWsvfiZ+EDA9enoZde5NtrRy/amOk+lcvZmGtBVvSAfakZRMg0gXDAUpkS586Tgx/fzCvhI7qr+tUobUBSjFLIV2p1V5ToKKFHY0JsuqSJ4T7eKUaJVBtNlT/ItQQBwqLICA3PRmsSCFRhUKcaVYnZlthM+Co4yK4wceU9LNZ6I3P+39fR+zTeVUPxouWnlD3HjD3hUGCghagtrYJljXfAAtsguhV7ooCKYV7BPjBbcCsJEir2uKiiJIBSUaSfE09QJUISSFZYHqsCRoUYiQCVggVK5klvj0DAkFF4GtKIpGiIRgE8gaChvjqcwjFpc4dGrQ1QidKVSYI2tAdTL/bt5TqjfcX9wXhXBLSP9/8/8cG4pFNbIVDdREgaQp6AI3ElAsjUlHAmRpQfXRmpElFpXD5k6ALiIqcZOljc2siDcTZ8NsaCcUObiWYLp1SMG1DfndMdk6gfECdRuoiYd40j0fIyBU5EMB2YIAV7dEgXeyQqmLYxzOaa9HMSNdpte2Sk23sur5/KQUniyUp4hH1rOZtCDGIlp8dYh2hM2NL+8XWbSZ3qcf/5iREz59CVsLIqmeg0ALKgpwwxG2G6CdIDOrDfR2cC+aPQPsmVBhgKsn2KHEpxlZhxI8ky/2QouhCDrYOdWVB9jZ2MKg7WljzKiK4kYN6YqI9jLj0/0KPw9TRYBbYHBGUBZ04frsQQnM9JB0XxU4tqje0Z+H3Bt9vbfNdmDHGeFxRD6ckC4IyOtQCzKikobc++gyOjpfrrcTTQ7ck49yS7pkWqm8/7sidb7+d28fW+aQ9/YDvSifh0aIdT7nuL1cdPCOgETnVHXm9+cUroz89bYzM2jxw0GHxY0m6xVkmxreO7ObQ5fR19B4AzC13lDsKktmDaGyDAWGisp8jj2KWBVguhRiMM75b+Uj4D0quukb2NPtL2W/WdEU2pQv83KCHDukaiEuI5C7KUR5NUeiABPm1IKMatChrSLSGUqzasaj3vvbpwaUz4zy5dt6lHU1Yx7U20cvgt6LhPeM5kA7n+qhXb8MX6BdGf225bpgle5v5x0q3jCPsOSiKVzpbHGa2FgCbb3jJHTIkMWlBTJhYUvjcxeDKgrUZNs3Xk3hJjRuKEQpqJocCTJsFpDlkadulmOzlFRM1Qs5zVAy71GhfDQcnw9eRsr7Xeu8mNt8o7rqUdmV9FMwpIwcKu2Xo/2zoduOcFOB7jjiezJMdw+hQhvjI+BBgNQSpBoiFYOEpUK5A11AkPoG1BZc6B12Olco69AFWKdoupBxF2EQIuWIEIaNZa/KJB0JadsKG1RA2wW+HGMFXCBIoBAtKFHo3LMm6GpUrtBtULlnJxTVgGwUVCwoW4M48NS3boYqBtGUe4XRvk53aYRJL+owXEGGQYYLCuPoZopiSFM0wC2wqCEhjzSbmnWchk4a0Z2oYtOIjkpYq6tYlbOJGlOqSpcqWZGg2gaTa3TqUBY/0Vfalyizzn+yzH/vAhANLhFs1WGSUk8C+roUpqyQ4JRC9co1QN+GnenrmOX3UDMYVOW45BwoMYiSvigtetqqlpn7mXWSM76dmh6femNk78D90o4Kp8EmCqlBMDW74t820UUH2P2hy/l0JUGPDEMUYhfUae1TJ21odCEEXV9WUrTCBSBGUVQMFbUCM7XQa4DkhX9etfJOUKVKR3X5rrAWleblDa78zdZzFpYU5VlQyo9LM3PWt8aI0ar3MCKm1NOxWxH72tr93Nu3nmn0zfgu/5bescrzV90M2TyBdDrb0Ni7D3oG8EwmjAsVrmFwIwGMOkaG2ww1BGs1aRZircFYRTGiEe237dHSVWLQtoZa4Mf9cNJiMoc4B0Ux3f7zpUvRI/SUS2Zpfc2m6mzveLXjLMVqheaKiPZSRWeZZSxpUtUpFk2i836ErYeeYdZDr7b3H5tL+fnt++A2x1AoTEehrZ9oBW3QuZ98FQm4UPzyLvPmoarepLcXXS1rYPf+thUhH3FIYqkvaPPsfW7gMbW/0HZx/3y2PFcfJdQsC8d51pIbaI9FXNo9nKIesWvHAO8bgbIMBV0qoaZrA8azCl0bTtOhgUVRk3jIi6tphIbpoBAKMbRdTC7GR8LLcF+kLCHegNQifdeIRdFxEZkz1HWKQ/vfQocMFzjJcRsKCHfjF7NRuCREVWOiSpfFSZPReILJouK1EQqZJUoYKktF+/QLHwT1HjmjHIn2tep9q5alwoB+BAT6/dRXVi+37e1fl0wUn1bhc7y3nHo60X0j3BvqPvrdcREbsxodFxHpgorJiXRBUM1xSzOKoRCTFkiya0xmt4pOhr57g/eUFjXsXovIVZWknjK8bIqkntJuxazPhn09XF1OMMvJqeqVBLIaUg2uFEyz5UskV57CocqXQpkLLp4W4l+uM8SNlPNjl7LK6yCEDjF+uZ/LKgjKuuWBEE1lDP+yRXx3jp7sYsb3kLzKKPQU9Io3wPMFVVzFj8EKX5oMgQgIjWBj1Y9aBy0h6IAuhCzXrCsqJHmdYe3YJ8xpKMs+ccYTRu9g7/p67laL+OndFTqugguFdKE3dpQFkyrvVW8ronGN9h4qdEHpfTekYwnZqGDaMfFQjGlbaLZRazZCc8+aID3oiCMYWwDVpJzMlkb4Xga7j6JYkWFzSJcpyAJUIuhGjoqFptXctHYpwd2CnlSYuxV6SrGhJfxm/RhJKyGtDzG1ZBFZbYhOEsBwTBhrgknQqUUV4vVrRoehXodOF9m0GTq7RiqHhEI+bMkWWXTVIuG0/kYU+PSk3Bqs1bj++1T6zKjpSgulQ7A/0EifHuic8o4/wPbs5JLoo4wf36RPd58e+8pDMUsTwyovuBZIv2RaL7CttZ8VC35S3R31AodRqgh706ut0PIH2POgEy/IrBcvonXwYtIRQ3eBZmp/hx3JUV1DuEljur17ttwu14T7NtD5kNd0aQkmczijsLHCGeWdV2X6okkhnvRVNFyoKBKNC0onbteh89n3mMzYjxJQVqZzjAEl0k/NEFU6BhLvJFDWp0bp0vfak3xBQNvZRpvf14zjKn++fk5AP/XS2ynlNQVgYz83SDY6Rv5Qw6zb/GB1yUOPXgRaa/xECZAyLWdFRL4sYWhJi5UrN1Bf3KaVR6zrNGgVEUU9oGMq5O3Qz8s0Xr+iG6P3X4xKHfH6nOEb2oQbciTLcM0mZPn08bdMl2JGlxmDjqIZpUlLJ6a1vkLKdmrB7DAjXEIfAc+GBalZGkGnX6IqVLZULrezjPGZkeVeVHRjtwb3JNTWanQK0ZRgMsGkjnjCYjoWG2uy4QAbK0zqiKYsJvVPQT+PyXN7e2V2vcKuVkigsJFGAsjqmtZyQ14zNJ2is1dEonNyCbzoVBl176EnEGfR1HTKI5J7MMpx9dAhuKix2xvhvUh4Yiy5M6QupFnEfXqziCJQlo6NSCUkUgUVlRGpgryXE76FsN20AdgTHvM3shNF7jSZBN7o673ntUDskIr1EfHdOBLe82JKZDAhVIOM4aDjGQS5r40eqDJSrcSXggs6ROVz0nsmPEvDG+duhoENpeFdTnwMrhRa60UxVMk+mRFFL7cRnzHbL40zs9pAwdx6sE1r6bgQZ5VnS8yIhFO3uMDi6hYxu3Z/qcLCVMe33rDGTSrsVIgKc6pBTq3SRQqFMa7n2Zjx8dFonx/eq3M749MXVS3broyKe2E1IJRpjYMZL2vl1CyHIdr3j6+YUhr/Jc3dpJZkTUbl1i6S57g9JhJe0s9qVaTia4HaikFZweQl3QyBFJ/vasCGiqKi/KRKAU6wTtGyIRMuJlIFBkdFQyNwLE+miMMWNgmp6Ny/8wMoKl6kTafeqavy0unbBJOVz1mP5mZ8JFw0hJEhLAJ05PzYtgdUyNjhCIw3wIdqZSTLT8KkIciwQ4Yt1iqKukGsxgSWOLGYwJG1AjqbarhWQDwuNO4siMaFznhCemsVNe6Q0Rp2/yoyUiFvaFAGXdfozN9Dqsw3pZKgYvGMk12o38SAiwVbEVwsYKRP6za9Shi9qhfQZ+pQ/t3fT7mgX26950AsfxTKccrNXEf6zLN+9TCmWTqz9i2UTKAywkjprARvjFMa5SWcAUl8JQqdKMTMPNld+50xwIMDVVLKpRLTXWjojGm6CwW9os3y0Sk2tys0gxq6baYZZngHdd4oDd6uophUmNQbwTbpRczBxiBGMB2FjTVBR2NDfKWE0AfwgpbC5LPPywVQJN7QVa40wmeKojJtXIsqxROrfjtdnpMuZq/j91PaIVu2w4x9iplp3JfbzwgUugCKqvj0zFBTvytGT8U7onseOvRywvuVIrwgZz5kSEcDGiPCcKPDwsYkE3lCy4QUuW+zIhWyuHRghHgjPA/QjQiVa1SU4u4WVNOrVio9M/Q7A/3J3BYwprxvS3ZEOdZJznaPWzvOCI8D0hFFtqigOtyhYvK+sa3LGtFbUsx7ua695U4UzSwi3qip3d1Lziy/tKKoamykUU4IW45oytNWbKQpKmVkMReU9cv7VrECF6i+x0k5QeUQdITKWiGOwEUBm7Lq7HPDl2XqOREMgsUrqRvlGNIdEp0zFGcUVU08NATW4ropuN2IlqjLiKv2hmCihY4KsVaRFaYUgvE3byuP2ZDWvSGpC1omIlLFrEorWgnBDMOvEK8Cnkro65SW9Uo7NqLrQpoqZspVqLqMDhE6EJKoICprYO+ucEbhqhqpG2xVg5F+jnzdpAj0jW9N6QDRuRcu7D07fppI2/qBt7CabuFp4aoXgXW9QIebHuDLSIeWMgVEpg1wJb061WXs3AgSCRiITEEtSQlN4Q31XlQcb3xXyYlUQc2kRMrS0SGh6dUel1mMql0dumuJ1jRRItTyNqP7TtEIO+hIWB820FE0zSgvRZAEbzRLoXz02vp/a0u/vFg/R1Om87oVQFEa6Up5Q3JmtLy3nlXTk2klYHz/Bk2FEk0wqaBbeE9sYXeekNSOhvIOUgk1aOWjCOm0Z3xWzqmAzoVo0mJSCJsO07SYjiPfrNl4awMKwda7LF62GVPv0nRgy/KJQS5EU45ks8UFgmsJzgi6UJi2N8S1BZ36b99H4o12U/a/9pGVXjRGRxoTGgiCfnRvjzYuZlD55oWCXtF1CQ1SCX1qTiVBR4GnIAollRNMRxFs8MaZC8FUHK4ncCgKW2hU0xHe3UZtgrANZkKjO6DSYppObi26nSFB1+eRFjGgcYEiG9KILt//hRfh0MqgNyj0/Fex06G1I44KSDKiqOjrcQiqZJiVueGlUvqs1pdewEbNcA6Wnj5hmj3VG5/YYp1Z++pFhUqLvdS88PtW08/kPCXLerows7SLVGmIhz7lZnfWehlgO7FsMQDZYk8/z2t+cbGhwh1TJfO1XWqxBIJLvA4KAirTKOvvm2jSpwv1mK0umI4iIwoxUFT9OOLMdNoSeA0QF/nI9kyjt7eOsr30pnL/s/btb/fecXvbFpXZEW7o7Yc+46S3rXccTBva/tzFzyF6bKuZat9QOp3FG/8VQ1hLdkTvPDQoqf+Cm/3ONOAqFqlbglrOcNRiLJgilIJmGKOV0LaWvOKj4GIVRWGQcpxSgYCxyLCQ763oVg3SiXAb60g3LhkMpcPFCjqzKOu8FkFifNA2MshQ7Ct25L5ihypApbkf29L83q9tK9hhRrgdSmjvU/CIg+5icWWKJeEkufgcbqCfe93Ps0b6BkdXfK3kXAybJmuM/cky/Is1uOEa7b3q5HWNC6FdN4iBZLOjceMEet1m3NKFjB/SoDuqCbpCMu4wHT/rlZKm4h8Wb4DrQjBdhy6EcCJn6MYuKs0w3SXc+bgR8kVl3rmaNsCrOutHwVMXkhKSBDlLgykW6ILltQluWrCM+rLFqE4KGzfh2lspIL+rQSuUCUBrwjBnKMgYCXK6WUCWGaY6STmn9NFql3vPeTXMiU1OI+wSm4KayVgST1IzKQZLTXsjLZOAKZeQSUDXhUzZhFQCWkXMxqxGu4hQTrgzHCHDsIkqYWwZkTa6khLo3TfS5yJFd0FAsSQiXWhQsaf813TGkmiSEQlKR5TtC52FqkArT+/vuIhcNG0bsz6r07ER7SxkU7NGJwv9i6llULk31HQxYwDvGw9gCm94+7yZckJWvoRElYJUIw6XwKL6JIcuvZtF9Sa5KFIpRfdEUQ9SapIR65yG6RDrAmehFqZMFQlhYOfVe9hVEYx3qf16HS4OGHtMh/0fM86iape73Chr6g0miRCrsZnB2Wkvk4AXB8kV2npjXGelMd1HuW6PWeclh/sT1d64NAvOR2G9GBtl5NwRtCBepwnaivAuUOOZH1+c7DHlscQoJA6Qquepmq6nE4pRuEj36Xk9BG0hnPTlwXTqCKZydGpp54Y/pXsTDi1nr303kRyV0UoycoEcR4AQdwvqd+U0bilVzbWPiKoyMqgExGhc6IWBenXIlRPQlPRH7yxxkcImAUEeYqoJOnWILZA0A7sbOWK3B/fmZNAaFUYoY7BDMcWKEexQgnaKwBq083n8KitQVgg2Gqp/CinuMcgiiz04Q+oFRWHopr5kVry2S/UXm4hu7WJ0SBhU0TpE2l2kzPVUaYHeOAWtFGsT9HKDkgCbKForQm+AOzwt3UF4d0Z1jUaP77RWu1eEgaVR72BHvBGrjes7wHs6OGIUYeAV08V5pXQ3s1Sim67e0Bvre47Bvj+rZ5v31tcl26ZEf3voOxeno99MG+CGMnrei6h7vR6lZdoYB5zxRpUTwfaMqwEeVph49EIA0hFNe4WQNxzBlGbk95po0rOa0lGFTSAfAjWcUq93cU6T5wZxinxjAmsNQWc6b9zOsEmVgIuEThV6KWE6V+B8pNwb/t7ItxFeQFVU/92tC+8QVNbT2/OG84wU66PvvQh3z1C3Mf5+DsS/O2z5aBTTqU2e7ebPrahCd0mBqhboQIjinCCwdDsR6XiM7mp0qryjIQNKgWIXCTaBdNTnO+8xEEFs4ceYmZTvyOEWFthlGclIi5X19RyQrGVTUCdQjk1FlcmwglbCZJaQN0Paa2rkrcAzaYd9aqStOpoLAzqZhlaCWzcMLYXpCMlmb1ibToHe2EZ3Clw9JFtaxdZCipoiXawoqoqgBZV1ELTBTHaJ7jToqe1LYdphRriLNGY447Dhe2gE3TIf3OeU9tAjArhS1lOXAl69smJOFEVmqKzPKG69nWDpEsyiCnnNT8JsxecARk2FnmxTrFmLqVex0TB5w0/koqbClDkVNuoZ4apPFekbI1YImhnqznuw4xNU91/ARBbOOV9Po/eU+l498d751lTBiA5YELV9jshQ4l0OuxC97b6h/ITJGLQpZkdjne5Hwl1Z/goUG1OYcl49vSsRcVAwKm1GwxZV42tch8oSqxyLF/pKXUjqAjIJyFxA6gI6RUi7iGiZiCmXEDu/PxM4kqiAoJhFadvd4Ixnb+R1ja1oMKVavLbUSIkl9zRDZtRVL+PTGqFQ0/dRx0ZM5AnNLGZde4hmGqO6Gj0RQKpnvRiUo4zMUuop+Pu9/zJwM7y8SlHUhK5zFDWvBJ4W6wAfgSpEkzvTz1k32hHrnJpJiVVOVVf6kXCt3e4XCe+2AaG6LGXETrEoTJmKYqIwR0cOlwOZoVcHvG932GkDXNmSerZl2TFFmQdevujLvplphHsamvQnxb08TqXE546XXnLT1IQTimASVLfw9an3JCjlvc+BZzrpzHkPeaBwoZ5hOXjoQggmHSazqNyiujkqt+Q2YGPeQCJDqC2bWhE1Rz8ZRiOYwkfC483O05PddNoS2tPiXI/GGGlftzrzHnO0D/D2oid51UdZdaQhnKauye70IOwQKJQxKGMgiXGjNexoFVKHTBaQ+Vmvsg5VOHRbEWxU6JbGBQ7jLBJ6No50FbYwSMsR3tMmub2JSmL0sEElIFmOs2VU2FpUJ0MVDjWk+xHyXr1xHwmnLGcDTBnPkttFoLUQRQUkvnyndXqafj6jOoaPhjscuj820Uv42kokvDcBmkUskRmfWctm7FNmLO8dSzMt4rYFjUCpXj3zHmXePzcSSBlxlJmP8gAPE3QW+hslbyjyukNqFqY01fWO2p0d0tEYFwaeou0gigsWVDsUTpNZg3WKTc0IxKAL6TOSvCE9/f4V4/WeJBRUrjBdpiPnZeBBQsFWvM4KblpxW0qDXecKWxHskEVXClyhKToGVai+Ua9dacwnXkwYh6dAO1DGP3e6p+RdvvuLRDDDGcNDbZKwYDTpkJicde0h7rIjOOU51T2xMaBfGcWFQhH7OeUehRnGd3+REaTicHVLUC0j4aaJAjaGdd8+wOZKlTzwjousANvxjFMXCMQOlwj5qKZQCpkKkEoMzYCwJQSRQ7XwxnRXUJJBPcYtrFMMx+RDQmc55HWIJkvjeRLP4trYQW1nKuCOM8KNIkly9k42kaicUBWl8JMua3X7E9Yi/QizmTHyJ9qH9pURpHTJyvAQ4wdGtPby3iBbcUjoKKoB8cQSKsM1mvsOMXEgZEsz9FSArWjCKU8RzOuU1BP69E/T1USTBpMK1XUhwxuHYHzCH0+Uf6lRRu51SaWfJ1egK16BN1SZV22vQD6SECqFXr8bKaVrjQpDCAIkcGSEpM6R2gBbaFyu+y/M3kTAaFcqdnu2QKQLAm375VEU+JJYytK0mvGsyuaiSiYBLRuTuYCODekUIZk1ZNaQO0Mupi8KBrv/e1pnBcH6FjhwNmXzcEK4aZQ8NLTqMXnoy8GY0JeD0V0hmhB0CkUhdDtgC6FpFWlaYG0OGUStFpUsQ+UG3Qkh117R2flvT30q1XNFoWeqeDPdSdJT/W8XqFaGDQtMvU1zI2ysB1A3uGUKaj6CX9dd39dlLfheffNGmNJxbQhT/7bZ1THTQCoH/6wZsOnmIYgqbKZONt9QKfSVzPsU8pmUsd7ccuZyNWOhUvNHwO/1XEFllmBTl2C9xWxqobLdoI23BQoffivVaX20u3R26PJ+VarU96Cc7Ktp50WocN6zhXYBqlTI9eq5Fp3lGGcxODouYnNRpeMi1ndrpF3QncxH4EODM+XERvUmcmqGeu30p2+fKKYNbeUj5xJHSMVBpiDdNYS+dijUTANvnt+M9vR8o/vRJu9M17jI9yMLvAtyqFawz9gmalXLVN1wZydiak2Ebgvxhg6mDfFf2uh2qf3iHJJlgCBZ7pVvwd9PYVAq7Ydo56mrztCnpAZRQW3Ip9zoVpdwJEQm6t6A76YPjVJ6775De8PbTnNgTTlOS9nURglJmBM7RW4NHQkpmBZqk/7sveyf3phTLp85/PgyZlsYxao059XMubFizsaz9uNfMEYLYWAJjMM5ReF0yaJTqNBhKLyGwm7sXB9g+2DKIdHmZSCgHOfziiYbjsgahnxIkde9JkIApNZgnSbNg34FpKIG3RHPjjUZ0JwxDiuQCFzNYmoFtlvWu6UUOovLUp+BQOS8vZFqdMeU9OTpyDmlw1wZL2rr4lJTKlAoa7xhHAlStajYIrnuixV6ZmLJbFOl8R96eyQMLFFgCUuWZ18At3TqI359F/oou61bTL2g6Pr5nenumc+OhAZXiyEKCBaGjDZSpOYYSTqglR/nxGCUJVYFVZOxIGx5Tapal8bCgjwJ6UaaiTgg1RoEXKZL0dxyIDRgI8hrPhWziDQ2CNBdoagb0kU+MGWjUsyvDcYJatiiqoKEDrc5wm5nN+w4IzzWrBie4OjqjbQkZE0xQstF/dzWUFlfda1XCaOs29r7rW66VHVGEFikLGXQ3XuY7jOnOOPgH+PKvGKAH23anz8sWkllzSidvSxP/avf84zRG/htay++e8cjGB+vUhvu8MRld7Cqur6MPhYYhLvSEf5nfAWb2hXW3LiA+l9G4Y47PY1FFJn4Jqrq6UmUneHqtXhV6ZaLuS1fxEbboWND0gXC5MqI6jpD/Z4KbN68o5r6wYXpidTEFImhpXJ0oWnmCVk3wnZCXzc09rVFjSkNb1OQBEVZdiujYvJSG0mjFCSqoKoz1tkhbm0u5C/dhVgp61HjPfxdG2JFU9UZLRtTdRmphDgU02W7dt8BR0+mJDesQ6KAoqG49ZYF3NNYSHdByPiBVdLREJVYgpEMlViiNVD9H0W0Dlyri10/gbS7JZMgo5ACnKZetKiIRimDUhFK+XQCFQQoVYbqyhQDtPIOlp4AkimXOUEVFuUESTvYqXEk7RIEBWsTYXOQUHkEjJ6oSQ6CROUsCScZ0h0smrRMNakEGcurE9SijG7VsSnI6T7UDX9vKJU4lVK+xITzOanNtQm3XDvKHb8MmFwS03xMAou32FRKH4Pr0dKmvdVSRobEge7VDO8Zbv1vmd7PTFon04bd7AP6j252iW7eSHJbG5VZdHMPMfCUdwAqo3FRCIGeFqpxXkhFFH3qsGjBBbqvMm8rATYWVGEwoY9Yq6xANzuorMBMtQhtSqwKNtk6f2gtZ002TDoR0hwX9HgTV40oqjVcJZguNSc+ciplVFyVfad6kXKtZhg2ZaeFAdKoIjqCVhs6ndkqrHsaeuWAeqJcMCuaoYxGRZE3hsMILQopBBdAdzTwdd+rhmJMYauWJcNNjt3rFg5ujPOnDQv55h8PZOqmBuGmNvFNG1CbO+i2JdhQphAUBa7Z8kI7zoEtmSGBgVoFqVagEqKt8bl8oXfaiIJKLWWvvdfRaLRJk5CJ2+ukMgqtNrJ2E6q9k0cw5VkDKIVTAakNyfOIQDuSIO+XlCycd1AH2tGIugTa0c5DNrZqtCUCcThdOmLVjP7oGeBqC0KJgunSYr1lM7bpfc3KK2fW2OZPX1DaoY0QBgW1KCMKC9I8YKqdkBcBCjA1P0cI6pnP2RzgYYV4qjQ6I43OS3ZlKHQXaVwYko4o2sscrlGgQi8y204jCqvJswBbOqa6C4W85iPcySah0gEblvneIRR1GFkyxSMWrePOqRHuYgFMBrjYoeoFQWihZGsoBd0sId6kiMe9IJiLymi6BYwQBBYVgqr48TzLAgoTonKNJJbagg71JGWyndDJquhehY3UOwls5MXVXCS4mqVWSRmOu/3UvcwFZEWAZKbctsxbN5APO0aWTrGsMckN+TKUiwgns4ek/x50bKEp4moxxX6LcSNV6vt3WLJ8I/WxDgvCFs4oNrkaqQTEuqBBh4pOqZflkV1VYxsGV2jW53X+0FnMhryGZIaiFeAyz2JAKYikpPiX8zAVgElQKsIZ7yB22qEzCKY04aTC1Cx6ZRfqBbJGkRVV9FB9uy57xxnhgWI47rBXULDJFWy0dRw+WUMrhxXjad14QzacETEzlMJtSk+rEgP5UMCT9rqRvx29iQLLhMvoirAknOCOlSNMJnXG9hrnpWP/zV9XU34R38lkUeHGymIeMbKO14z9J4+OLEYpYuVzDf9SNLm2egB/yRZyydQRFLWwr98meEN8Zv3w3g+zDPGSXj1uq3Ql9J6sWMgamrBdeuJ3EyitUWGAikIksOQEdCUkdwG28De1v3LraWbl4GWUTOfNl0rZKOnXZzc4IiwiivGsytruUOnJV33xGFeWv+pHwl3Qj4Tv7lFwAJ1Z9EavDeDGE8ZbC1CVhM6yiE21Oh0XoqsFJkjRriDerBi6TRH/RSFTTbirC1O9ibyfZCog6rWOMegk8RO4Xp1lY3xUMXRlJErjYqYjjaGPRinrfMke65BmhqxpQbMFQAtFi4ACod4UEjyzoaa7DJuON8CdL9UXKkc97KK1MBXC5KwZ3a4JX5dSo5gujZO3Ajb/uYa6O6HTNuQHbOUZdr08r15Y1C/u5SsrmWVb91mgzPiW3hDX235GpHXe880LzOYWwdrm9lzurgulUKbnMDLTed8lNVzNaFNsSek3Pk0ChTfI8XmzIoGnx1oLeYHuZug0xzj/bsmdYUM2xJ3dUVRXUF2H6mYQerEuCcsKDU7KCI2a4Tzx5zEzKCi9sjK9ZVr58lvWIEVeipLtoejVde1Hb2GOUJtSfWVZpTWKMhKuFDbR2BiKEciWa4phCEcK9tt3E48ZWYvVmtovHawN0esU0S1dgvVb3PtOwOVzXbRaIWGAij27q1fzvXdfAYRRwdBImwULJ2lurtMeGSFt1Py5Bw9RKlmvTI/yJSILq+lxwmbqbIj4yg1J4NPBRBSBcWgr/ZJizIqEM22Ab6HX0Y+azzPu9NXSRTEt/6a2PkaV++9FwmNjfTRfFNb6eZ0KnU9bityccxlgz4dJfZ/rUvekx3gqEsCVzNW6Japn/fujsJqiMJ6VWZQz0arDRV6oVGcQNR02UqQGevfoonqLR9TX4kSxJhnGdQyEQpTkVGI/p/KCh9BVEHR9JSYbQY43xD0TS8pUO/Fpd6UgYjvXXmMqsQxVuowmHXKr6ahqn/WjC/ollaXM6ybypQYTU/Qr3fgPPoLeK3XaK6kcOxbU2uxb38QtlUUgETrdA1LRZtZG770zogA3UsWONTALLKNDGQuqU1R1iihF14V9va5Y54QoEu3bkQjo6Wq3LYkdRVvx04bM4LqmHAZ9JNz3R/n+ihSuFkBUOhotIELQVESbfSRcVwU1XKAWZYiNcKMhLg23vKptwoNrHWqDriSoMPC1+EQx5YS2C70om+i+8dqndCtP+DZl3nCkCrJSLduicE73X5bxeM73/3wA74hapM6LeeWiuWViEZP3DBGOG9bLKB+K/porhtdx29RCbr57DJmMuKsxwp+nFrKiNg5AWOZRTRUx6zt12nmIW5Ng2t7wwAl5HjBhq6UYW+rzoueJgvf+7imlB9pi684b4VPKq8DuJhBVnm8U4AKDxZfFsmXZMGbMqXr1Qj19psxV6+UvTykm1wa0WiAjOYv3NeTDnspeCzOGoy6588reedmGvVzzwmlS54XbUhdQOI0VxfR/uyds1ZAvjHEVg4pjVCNGxSF5I0A5Rdj0lCWtNCoOvDiQ9Q6taBRqwwWhTEfTFJQ59sb3gYYiUDitEaWxxuC0AaXLknwaTM/wxkdqA4cohRZHWFi0OKJuQW15SpSm5M7QcjG5M8gKRXeThht91P3uoGBKVcmdpp0b/9JxARO5oeugfYPCTj1EjX0/IOL13mWGF1as82JagHQipBMjXS8Go7Sgg5KbpqUvIqJmCLr4Denn3PfqfCs9HSycua70RI1mWuszDXLBC+5Z/90XR9qToBWEoU+HiYJZhqvydRE9Y0F7A0+Jr2rRG4d64miUqrmY0qgPA6RwtIsqd65dTHZrhTXpMK2JGi6NURscWhwkgkSBN9yYdoz4vP2eUJsXZOvZNk6VUXJDP2rvnQYKF/rSOHQ9G2gP7LES0p84+Xt7biS8rxBvraeOl6wGCQVXd9gaEDmCKYXpatKJgD9vrhJHdW65O6RzexezbhK9uYXK70f6hfIK+xIZ/x2UpYfEV0OR1NMemxur4CCdqPgolFZl9D5ExdGD1lLbBKMhirzDIg4xRiHaT/x7KRAAgfbtGOmCivHss1x7qqWz05ot020hvSbpG+J9lAbGzLfrLNO9HxEvn4wtbeYZc4PAOGpRShgWhCVTblohXfrO+9B4mnpo7MPbCL+3NI7+7yV2dIWFnXisvObH96KicIlDxRYnkA17vQab+DbJ0wBthCAsMMYbwRKCNuIFu0KNc4q8CD3TvDTmXaC82JqCdh6yPhuiXXhrWgKB0oHrUyRUKfamUammX4m4HOe9boSCTJOlISawBNqhlPPpmKHDAkHoHU6RtmUJQenvRxf40ohJGdmu+JKu1mlaRURuDa3MR/rb3Qi04GLnjcTAgYaokbIwaTEUdAlD61Nq9lAHr4kLqkvaqL1geGmTkUqTEdMm0TmJ9lV5HMqnBihvf9jyu1fBR1DUTcryygSRKrBBQEqMTQNmalpkLmAq96mxrpyjSSmcq8rSi6Gx1BakxEOOeKRLozFBXOnS1TGTUznp+u17TzyoRriuJOglY7h6QtZQZC7gbltlylVoOa923l+3lxNe/jtUXkE7VAU4aEpC20VY6ydbAPGtG1j8jeVc+5MnzVI0VQKLeiJHYuj8YBm/kWWYHPZuWVRhkSAgTZZxc7DcH7CMaNhYkY54BcZFdwvBhikKvNBP2gm5vbuQmklZFk0wbHwU0+Kjfj1KvCuN8baLyJWhHmRUFrXpmAo6D5HqblTHzyhcEqKqMTa2ZCrEuMIbYeIdIgof9Tbae7AL54XAEq1R+Nzw9C5Yf2VMelPEisOExS8OqTQCtBYWJU0KY2jmMWvbQ+Qu8vS6wue/pUXIZJ5gjNAqIlLrDXHtFLuzhku+MGbyyYtI96qCLkWbtMZkmrCpCdcKSivUmhC0YFoghc+HSpbCysd1GVnaKu0y/0ykLugrzKdOMW4NXReSO0PXegaDLy3mcy17ooR9Om0p3xloC8YL8NVMhwPDKRaYScbzKrd1a0wUFWQiZNPNCeqXARtcwT3FKKErcHlB0cpweYF1kOWCtWCnILvnIW3y+0ZpHMjMf4Ont041US2FG63jxuu4zRGEDl2xEAuSaaRQ3jDvvahL9dPeRLXnAceBMr4U3JbLe7WA3QwKzox3t3fmW4VK/XF0y+wWqfb3G8ZAtQJJDNXIRyFLGq0qBJ07b2gHuizjghdi60Wqy7xtUTP+bQ26EqOUYWM34cf/s4DgHiHLQ6aaCXkeoCe98JcedkhsvBNSlQZ3WbIEUWXequqXKPM1X73gp43VtNEugFEUlQAXCjoPMMHu6zy8T/jQ93QEfL7JuxMkL2vyFaZfSk8SIV9qyRcIZgqSuzRBUzPZTPh/6xby87ZhqhOxafM4UaeNyi26te3pF2IULgmgFvqSpon2JeQyIdnkVe6lGXG3XYwasugNCpMadKAhDtG1Gpqd7EQPAtRQDZUkmIYiSiAIi36JTynZeZGxGOWomYyFUYuayZACtBNfnkdm1wXvWXmqRzvvtVHf49RbMPeUZpZZ7HkHpfdvoZxM+b+TyLKkOsVQrYsTVYrXakzpSNDaU3qrYU4cFqgwx+zGVU/uFVsKMm75bNxHGsf86VL3YRz3asFt7Vm8l+16aRCI+LSO+7uP+4GeMFs6Cq7hdRlcTVE0DIUorz/UCWAyxEYOPeQIAocxlkqcYbTXIwq1n4vebhZib09QImUJY68+7kJhspNwy+QiJrMYYxyuWpTlDiHPDUUeYJsBKtcEk5pSkqov2qy0Z9Dolj83m1iiqCDRjjgsoArWaSpRTj1KqQYZobH950pbr8AdNcWXS6tZagvbKCXkhWFTu0K7G9MdT1Cp9mKFkYNGQVjJWTY6yUjcYTRus6q6ntGgxVClSxY3vFDp7oRtFCmNRzJGHrWB+DDDotok+y5ax8JoyjNulUWXnpLeSOZEkWNwoinQZBJgRbEktlRGUjIXkNuAzqKYwppp9i2KjWmNP00uZlNaw+YaaYW41Ew74UWRVFPGlm6iUekwmjTZd3QdI0mLdX+p8Nu7FrHuD5Xtao4H1QhXYYCrJ9ihBBv7Cxx3VboumlWerAfD9MAbqYJQFUTKkiuLxQ/eMiMSLpvGGfmfhKFG4h+M3JchcXFAPhJjI03YLIju3AxTrenzUgqxDtwWdXS1ggUjdFYtJB02VDbk0O74bUSQ3DCeVSCajnR7qojuK6KDp6NrxKul40WrGtUu1mqKWoCEu08kHKUgMD5yEBis0tOR8N4qzI6EeyNPlZFwT0MrmorJGwOav4R6ENCdMlgxoIRqmNNQXU+l64UDxT9EvUh4NjMSXjo8EIXejaeyrmJI96rSObDhF/RYN+OKaBLCFvgGLakyRWkgBkIwAsOHWBatykv/nh96OqUzqlsqnzdTjS40WE2RGnJrZqjqUtIJpw/ej3IYh4ssEljCuGBBI2NpnKLziDVNTTMPsTdFpL+pYm8JUYVjopt4um+aIVMtyDLEWl+z2vWsxN2gv+Y1Gpy/HkC6MdJVSNc/x6pWoAJXlg6b4RXqeU3Lv/sDeOkolJJyN+1Z99+lo9WLj0Gfmjf7fKbV7vUeGwkvU3eiyH+XQmiKGUav6jEXvJHsVXH9pEvEU8kx3gAX4xXWCQyE0MkDNq6rkLdDTCEEbecnW7lDS47EDonMFhF4pnPDexHxsqxfL1wugY+69CdsJc1aQo0zAoFGPxzU0e9lsi4IqoyE+zJ6ZV8Ggq07/n/2/vRZtiPL7sR+293PEMMd34AZmUBVsQayujiIMmuZJlN3m6xNpv9UX/RFomSUTG3WpMRms5tkDaxi5QAkEvMb7hDDGdx968P2cyLuwwMyE3h4iZdVDruI++LGPRH3TO5rr7XXSmcZ3zvCBuovhf5R4IO/XuAeT1LLnsBv7n2gd5hwizDKHryC75Rqp4zq2bQVYyfUN5l1itROjQmvKqR5ySytc9bKsGiQxkCHuMzhzm2Ks1DavhofWfiRpR9oXERU5whR4IjFPoDpyVm9lJWObjd6UN5Mv8Th92cgPscHcADiGUSFQGZdDZw1HUP2bGONZiscSLmgJjlv7RO4fPiMv0vjedf8sy0av6qNA57bLvWr33dS5fyG+3VqgziOunzm87yoEYtcOC0U30YWtaXDuKW9182uZb8PuF5IblJ2mBHhooo0pQXjtDaD2M9XJ2RvLa+m6OTQSz16rvuWIXprkwjTulPIyQC/9A7fuRI9dvT3TkXzBG4wMzb1bi6GUYC4qrCoRlo/ElyaDRQBm7+LHF0ySJ1Zt30hnTzjWDH0AbfxhJ2pAFKT8bX1jP/e6SPeXjxl6QYuwpZWBtoQ6Xzx9XlVxm8wD4Ymsny4Y/UOnFUbztot53575zUTISVg7cuayWLqUICIp/IDK2+qxlGLsrOQUolyHPeRX6QznGtQCcgGiMXjp5AswSdWF3tOzzdchlveXjzhfnWLc2t+clPhH327toAXC8JXK3ZvndDd8+wfKpfNllZG2zmTHvNoJFxxRZ9cAR0DtqMmltn5TFxV1PcukfWKtGpI7fSxK0SVVDnGlScHEPX4yzWubWYGfb65xbIAmCqFIuTTBXFhzorj2lO/dkloam7u1fhFzyoM1C7OcWQAlUQqYCSQnECGykWrzkhm7XvO2z1ZhSeL5St1kSjGJuGELPY3++QLEC4XkDBLyrxkgjNnRyda2gRquqQka6YgqfVvbHPDPtcM2RPzBMjLxZE8QZQxOeqSeTpVqab3EdE7y4VXbbgI1a2Qn2J5lo09alDiGUilLPzAZb2j9QN9V3N1taTrKjZ1zaMvz4xJdcxRVx0V166lL737N3Fh0W/JM4w1Y3GiTXqYxI4Nv6Z5YnSOPkJymZv9kk93lwyu4Wpcstmt6ccFaRPozx3p3cJ69B5Jgg6g2wxDReN6zirLDh83nt1nNePGo5rt+vu+5XTf41CxfkzvFOczuciW579ICigvsvRnWzGZ1q5HwPwrrupHoO/4VLeiTAHikd/JNStg9x5vZoLqhOztXMuhVCqKDJ0Ik/387KI+DdUSNWagmFRk0N6RTjLxMqE9IGJmOUBQtRgrL0VufmAKp5aA2QHdHY5z9mYClOvDIstFA+nqKMw9r0Qt6juN54CHOyMrxGjs6d4hV1vcOBKi0rQJ/6VSPRXCpx53JbjrvS2EvT+wcd/m3uEgV2LmOkGQLLhohUsXTWHhO6W+zrgOqr0tti3qyJHbb9fn912GBiGuApxU6CpZ73S5UbijWLKqzL224LfXuCJr1uN7zzHTPX2vYi0wOgsZfo0PNm2QZyYRDqBdKakyh6J85RJOlOhcUc/Z5xuTN8xHxjcL3MpTpFR3QeCrOmYZwjPPfe3rn/+a57VLHY/ceuJZi7aeRTNw72TDohnprh3XHwX6m9+ABHoe2/49zdkT20wG5zKLaiTNhIHF8FFl0gJzLi/KS+cyTYisqsFaH7Mja2XnXW3u6uMa+nMlniVjlIH9YH5NzilSJVIqUYepxO4tE7HN5NrjRkdq5c59O9WQ1hldRVzIjKPnKi8IPrOoR+oQqXwiFhUigFtEUhZEzekdIC5AfJ6ThZZ1Ln5IsDvxjLVH60y9GmiakWU9khF2yeTOyzyAM+bd3uR7OTzfz3jeNfHsz8swr62AJMfeVQwaDuC5KJBdUeU6K/POEnRBqcWSe1QNa04/r8TaX1Rlfn4dOt5aXLFwA6nyjDQkMxVgEulcrLf8ePGY83pHReTpuLJ25r6my99+nnihIDzfO+XRf1Gx/VFk+dqWf7j+lJUMJHGzdACYmeQJgFdiFYRBPVkNrHW5YlRPVSV2DwLte2+Sm8BwVpEaZ26FtS3SpoxW9TCcOIaTFW4sTF82VluSTbxzAddRTGGE/sSRa0i1Jy7OcOMp1+97Ls+2XNZb6wUvcvNK0tyTMKrH5YaxxKs1pU/hYX3Dn55/wvak4V98eUpuwqtznThKTI9j9J59rokxs4/V3P8ulFiyUs1eVuMsjbuJLdvYEIeRMe9AI4N6rtKSOgWu0pJNatmnChF4sNii7ZYuVVz1C7pU0foRERgL8Pelf+xVr5j7vbD8BGo105Hd65DXkJeZ7t3EgHK+vOZP7n3AG8trPrm65N/+4n2uri8Y04r0n95l+Rcj6kuvk4exduzXnrEWkgi99ySRu+w3kwSdAiaO2I7SExNFGWkQUbphyW5zxmJIxOjpOutTijX072XiH4Jmj0ZvgDKC7lZohNfWV7z1+hUP11uu/3bBh//ihJuftugYybsdxFfTRMT2nTE4dRXxdSQlJUs1i2tmM7asVqTId58HJvX/LF2f8kLvTEsFiN9RJSYzi3GdPf5OytFd8aOovMWTVI5c2c06JzMYlJQtMzwlKFniB1MXCvgWiGqS25iRIcIwkgMMb3n6txW3cYRPA27jqF2ifhxxXUTJSOuRYI1hWmLQJpO46d85OMQrqXHEpTCuhGqXqTaZsFPrB28N+Pla7pwDv7PjmxbrOZP73uLahh6/2+ODw1dK+GtFK5AR/NZydyVb+4E0tfkzxPjrS3GPHXaDME5Oub5cR3sldIrvcvlSqqfRfs87NAQ0eJOxny/I8eW2k6XGMzxoSA+WhIcDTTvgXZpVZgC1Tyz8SO0jSz/SSDwQGoq1yJRiOSX+S4sKSoq52syA/wZTqqmmZX4sz8LUJ15AeMqOsSgEV8HAkmR44pY4Z4at26Gmi0qlFeuzmvAwofsevbqG7nck8eFXnK8yyRMUTFr1zOu/rl3qaMSzlt2fPWR4bcXpvUf88R98zlv3nvDFX7b8x//LBV/+uiBcFU3pzr+/zxH2tn0XBVclLtsdXQpc9y0xerzP1OuBvHCIy1SVgafKZy6aHWdVR58DN2NLH4OpKJfQXTi6h+De2/D+vSu2Q83VZsFm0+JDom1HmpDYdjXjbWM94IvEyf0tp23Pbddwc7aEzuN6c8N2vTCeKtWDPZenW7Z9ze3TJfQetxo5edBz2e7ICF0K7GOFE+XsbMe49myXLTttqNbCcJYJdZp9EdZ1T+0i3bLi6bKjHwN1SJw0PY2P1M7aQZ8MK/a+xqEsfc+Q7LjeKUC/CuPXPK9idtyMLbuhomZkkxrW3tqa97km4vFkWjfisXStQJ5Ju4WMVqRRR1diGx0Wh5ixtXG2Z6ibkbPzvaU1ZSHeN7Nh47vMXvqs6vjR6oqzuueX/Sn/5uYdPu7PGG8iu9hhuam/+XixILyt6O4rp2/e8s75Fa9V18Vs7e6Hmw3NZKrgGls6Sdbz3GctOJeJCxjPTG5urLXJyuJCSJVVq9QXpqoyQD7l67nSo2k9mFokhSUn1Nl2UmuTda6MnUSF4Vx52PQs3YCXTEboc2XAs0jnoUStqStS+ogjc+I63qiv6XJFaEc0vDru6FAu6sKER3Vo9sX86/AaOWIFp2xwRRhSsGpU0lKpM5l+pxXbLOxzxaCeUR1BMoswWL54zBYHF5mNXHKRwE+sO6K/yXrhBzcmJty32Ll5v2DhAGmp5JAJJz3333zKOyeP6B8Lsh/pvaDXNfJpxe11MRxpjYWLC+upSgtKlqTO+bfH/QOzbHr63lGYQrtOkkIsi6q4g/HLFWFni1bfKy7BeD/RvTMS75nLfU7GsmssUu3oyOd7Tt7P3D8fIdTU/8ZbNBFYfNDL3eUvfIizcz74bH3Jk/05h+IeZd07exhNa6zpOTjkjh6z4cfj+LXTvyP40R5f+R35dUMEde4O6J1Mz7KCm8zRopphjRwWITIFJ6MHGVnKkLM9+kQuTLjzINegUUg7mJRSkiZXFmbmT0VLEUbma8hy4CfJozEwuT/MM/bZHTkI7thw7+/qUC0ZP0AE15kCyvE1ixDvzRDNB4RkSgb4Zrb9OQ67KsaEp1pmFTWxqBZmNjzhuoSMmdx6xrNADpj3QFNB9XIvNvVCWgXGkwpZJgjMDPgEwicFWu0SoayjXDGbnZU0cDhfj4nN46fm57/pBL37s6/dG0cJEVPbnj/6jHtXlRioyRDLE7Man9V4ZFmY+d9Ro6nnDin/+yZy4WtBix1JbT3DayuGd05xb2y4/49H3nl9gyalWZ/+Zp/nJSrVyhLavD18ZhmGOU1Hi4qirk3mPRn6AXiXaX1kFXpyLEa+ye4iuVbSQohL5eHZhj88+4Jf7s652iwslkrAy0AdIjupIBnATi2cLzreXl9x1Vhvb9dXDLuKNNRIhtQop8uO15YbPsmn3I4Ov3Wk4HGiLMPAkD19DPPnWTfD/Pfs1uaImxdKE+xeWPnEuupZ+YE+m5R9yIHaRVofzasHIy73qZrVpl7M0I2j+e93bSSMCR9TZf5G6hnVM2hgrzWDBkutykolGU8q979i/EiiksSAJ+JLikqmIqGYbD1pRhFaP3IW9rii3J1eL6L4CYS7nnfCLadu4HZsuBpXfLi/hx/2VDnhfwggXFIm7ITNbcvn4YSf9w84cZ2BbhytmP5kVTK3K0mcuD21JDq1iK8x246eMsDvrXZ89PtnDGcN6gwkq1db/FRq3wtztXeKDJqa6Y+lnVLyfO3EPTymRsFbj6eUPoB8b+SiscoWWmTzZaLzong7jECJXMNxm+zifRTX/Hx3j5thwXjV4vpXs6qratUozb70hB8u9kmIqwpjcZSqXGIVeoIkxpDYuMQIsxx9lxz7ZEZrQw6MqvSjZYaO6kh5kp4Xmbskk1+DvfcrDjxCG6lf3yJvK+MaqktjxJMTxgDZWe/T07ji037kVhvWq47X9YqTOvGGjJzsUsk0LFLYGvoTi9LITolBya4sZo7E+zr370MuucroQVwgyhyBU3XCovVUvZCTksZMTsr2xPPZumFbe1IWBibDPAd7oIcoNY++OMftHTdfVvRb0GFAx9GMXl7FoUVmPAiuLq7cHBYF02Ux93o/+2dOSuqje9EE5IDZ62iSsEsqbNXEYIk5c/vRCiJ+0Ls9ay/TPff7HBOzXVk+uJQiqmWCm6xb1VhpKUomsgGTI8xs2znapknbHWSP23j8lcfdOvzO+oKtXazI4GUy2TsUV6b9O7PsWt57qrJMqobZsI1ZcaXlc+rfhZ7w33Q8zxW6gBJ5XrTht+lNddbyk2vuAtFp01Nx5ej7Y6d7LaZ8L3UcfeYc7N8TaM5F0qROCJKt8C/KYNJA+hzIJct+KlrLHdb6mfFVNH73h1OBcXpqvoc9Zz4Wtf0nJvNM6qiJrFzH0o+M3t2Nei0byEGIS89wCqIe+V02MXxm/EqpeeNJJ60ZRpZYK3WwqEYu2w2LMNK/5ln/6CnDw4GLi1tSsLXodrGgf3/B2C2QbcZ9PuK2v2IOnto0W894UZNbj9tHwpMe371Y+dWRTzPDELgaFlz3LU9uVrMjet2MlssNsxEwwM3YEtWxizXXfUs3WoSuc7YmkgyfPznl38ZANwbiaJEIIkpTRZbVyNh6+pOaVHl8m9gOFZ9sz7jet9w8XcLemHDXT2SEcH27JGfHvq/s1t8qrjUW/432mi+HNY/2a277miEG9vuaHB3aefzO4SLkXuj2NVfA0AQWYbRIX0zhElyevR6CZPapYjM2DNmzizX7VNG4yPWm5WyvuP53URZn69EgGV9ij31huZ1aApNJ0CESyJqhgHMoyyvNc+FSihFxpqQJzQhGDmBb5xLQQUWE/Y6Ux+n3vGRWoees3qOhJ8uv6dfwnPFi3dF3A4svITctj/aBf7P8MddnC87Cnrfqp5y6Pa0bOXF7k3XLyImMtJL5Mjdsc8OtLuaKx6ief3r5Ef/l/+Hns8TguAdgAsX5Ga3fBOCnn0/j+HXHr2nciMcMWq7T4o6+f8zeToDiyNfKOEvoE4LHWPJdbng0rulz4KebB/zFx2+QrmvWPw/4mxteJfgxSWGzGrNNUvpi8AXzfRonSlZhN1Y4qbiod7y2uOG83nPdCh85z4hj1MBVDMQRtrHmelywSzVj9CYJip46RNaLgSrYBWcynDQD/5QFs2V7dSfo9rzn8s++YPEPPTEIXW2P+1TxeFixz4HoPT/tHvB5OkUUXn94xRv3r3hTOv4ZV7xBN1c/zZA2M4REckpE6SVTOi+tD5y766aMEEtziAN8AZQepRHFAU12rFNFnT1dzlzFSK+Zj/WM/zG/wyd6Sh8D1/sFRItX81eCu3H0Vyv+5umP+Hk9ws86+OIp3G4MgKdXYLJ4jrOsixD2UN2ax5dLB1J0ZprgSGIuB3O1CYgV5cEEttUwoWVfO0qvqoG8GXhOJ7ozcBd2Sn0DfmfKhPnz/qbuuT/QoV5ssbkIqC/7oc923gZhcvZ3naDJkImB5bL4L+7oNsqjEztoNUis8F9UVLnC76B+An5v8nGHs3gy74r5XS7mPod8cDPZ09ITLnM2uEulSDNiLU8xQ+1KTA4WmzOBvVf02Lzw8TxXaDhcf88WLcTNxZavAPdvGOpM6TYuZVYpkKfiyPH2ZQbguTIHdYnlWn7Jxyx7y0oeV4JfAJ6iDDP2OGeh9ULrRlahR1XYpoYNwm1qiThzfgYmefjh7zwiX6fn3NHfN/mGlMNyB3xPP8+WCHG8zZnYQMli66Y+Bdau4/XqhnvVBh8TP3P3D07r2G7PjaN/UBHV4+tI88VL96N/OePZ6//XkJqnk5bujx8S7y9JCxguMqlV3jq54v03nvDm+oqh2XJ7smNoGppqpG88H48XfHF/ze1/dY/dP1kTftrT/Mtr3M+/gRASscQWEeKDJbf/9JLh9QX1J1tO/ocv8J/uXujusFYjG8Ou5uPrM7bbFj5tqLeOuM6MbyhtPeKdUoc4q2Y/25ygKgzR0/UVKTnS3kOAuFTcAP4/L9nokrRQ0kVEFolQmcz7XrvlpOq4XOwYs+e2b7i6WfL0yRpuK5afeKqNFQpysMfqVhg/WnBTt2ilsEzI6cD52ZY/Of2Uf7j8mL/cvcVfPnqdq+sV+bZi8XGYt5Nqu0Ykw+Abdk1FtxppQiSImRSu/EDjTSIQSsFqnyqedEs2fX3k6A3xiwXNdSbcdC/0uPxQhpNM60fqoCz8SOUSASPmDANmUxhojWIxY5vUMuRAny19aUyedeh5Y3HNOvTWplnM2AArzGCkosMY9CCJBYOpnItqW8F8sTDReeVHHra3dC6wbzNPfeTbHoUXq5MeI2GrVDdCqj1fblec1mcMTeCN6uoAvF1HK4lWEucOKhxdyT+eWPAptP7Hiyf8H1d/xfuVAeOkBwY6oeRy4xpRkipehKpML06EVuxPdM90ZY+ayGQcDl8m/E4j1zmxzY7P05r/af9jPh3OrPJyJPeagL0vhQCL4BB2ueZ2bHm0X5EfNzRPPM1TRUre8Ks25gs+W3SYPgN/jUy1hYG9HlZ+4LzakUIgSAtYxnefAy45k6PnwJg8+1hx07d0Q8WyGVg0kYZ4MHwrFxocmPBXuCWc0ERWr42cvGtFoClC4XZs2VIRo7UAPI1LNtpyEjreXNrN451qy5+svuRH1d2JMGlm0EgiM6qyVwPhWSEiMxZUmA1zhiNTC9vHEERpJeOBRjynrqYRzzZnvkgjO82Erucn15dcdQsE2PrEWJjw0IHbQuwqHu9r1EP9+JqT3RX18Aqd/89zllUzRPODxZQY4fOck7Gobb5ynhZW6ivAYlI+y9HvHsWczfXDQrq6EfxQDG3umNcKv5F77g91SHGdDe6wTxLgTb2hXswI0B0hiCIdn5nxiaCbd7XJ28Urkh1u63HB43tzR/edFoOggwyeiQl35b4zHTc9nBZz68H0fD76mo7/zIRzxLb+PRD/JldoOQbhx/vseB9OJ8evMVQgeyktanaNSTH0e96XSvGY8YJTZtXFSx2l6DQx4YfWa0sPSWrO516ypcngTUGYA4OGYh5Y/G+KIacB7+fss/LcvEcnCcfxLn4GN+oxCz5fZ+X+VsJskgpx6gl3HedhxxO/LMDiAMDBruu49OSTAFtP9vK7B8K/7vr/FfcCbTzx/pLxzTPiWukeZuJK0Ys9F+8l3jjvGHTkJKeynrB1xSa3xoS/t2B8YwEZ6uWvOJGn69I58rJieHPF/sdrUCUvXvwROWbCdXQGpneB9tZR39jniSpfiSJLhd0ex0BKQuyDFWWjRXvlSgid0DyxvvP+XIhrhyyjGcCFkZUfWHm4qC0N6RdywaPHJ3BbEW4czROluVFiI4wnQmqsVlXdWLE3rpS4ioQ6cdIMPKxveCs85UN/n5g8ufP4jad9pLRPldgeopARIewhR0dyxUQ3B4IrSQfuq+ul/Vix72u7/osXT9g6wj4i/avps/OrhqWRGvkZjownp3QghxKx+0xSzy5XXMUl+1yxjzWPhxW7WHGv3rKsRpw7NmOblmVHIFxyuedDK3Knk2fqHZ/W016sfeKUDhfg5jswgy9Wjt4NLL9MiHrC1nGdL/m3p2dIm/jvT99j1Zit/r12yzIMPKg3/MnyE16vruiyOcw1bpzjv7I6dqnhy7ykjhsSQqcHRhZkfu306AurB5S+gHH+fFNkQCpgBMAR5+c79VzlNV2uuMpLEkLjIlmFL4ZTczsfV3xwe8mmb0hZGEve3Dh6xj6g0eFuAqtfOpprtf2xfzXl6DDJNb7mZzqtj0y+ESRbL4uM1IUpB4jq2QwNfR9QoHUDTT3ic8utthYRkRwxO8bsCAUIzTEqs7O3zBfPqziGGLi6adg/9YhXfJ0RbzeXB82Gi7Bl1MA2Nhbr0gvNTtkx4qqWf7/0fFr1s1O2ZEhkIomEklB6tccsVrHLpVAyHUKT48gssQnlSvAojSoeK4otpaLGsSfzlERH5tN4xuPuhE1saCTzB+0VzSKz14anD0/o2ppFSNxrOxYh0seR63uefrM2Z/RuQOIrwIY/MyyPWot02eRQMyZwanJxV1g0zASSaMybMoFqW7jq5GxfIsrm95gI3VIxOfSJ69FrtAC83yEQJ5RIMGOsNRQn9LKDFQNEqSnPI/hKcMkdwPJRsWTCAibvPLDhBtwLGzpnfx9eckfaMIExYf46xhoHdYMtyHIoxqAT4102raVwQHBQV0jTGPMVR+4YbPxdG0VlokffT8/P2C6bSZRoRo+P8a/a7rMgZz5WEKrE8qQn+AS3tnBLG2f5vSNzm4cknc+ROSf+JQ51kBsltYrWlsTgJR+KTGrxrksZOPEdu1SzUZOrjrmsj5QZvT/bNjOxP19b0Z7m3fm8PxRLZpl7+SwyV6bKa0px0PC4zn4u7mgWOrKEs3/7khSyBNfcBWe/M+N556Z3FsfoHbkWxpU9Op+pmojzyvq1zJu/94jm4Q37RnhyHuga4XK1ZZDAk7hi0MBtahhywIsSnBFGJ77jvcVjHoYNQxvZ+OFoNfzVkWtPvlyibUV+raE6T3DSUS9GnH/x10Ax+7bjnSElKxKkVhmTEBfgQ6IOcZYVg51uXhQNCeem886VBARPTgLXgfaR4DtwA8XzzpzRT6uO15obvhxO+MnNfW66htttC9cVYWuS8eFMSAsD32Np93MRfFfywgeQTWAYHY9rMxse1LPLNfuuQva+tDkxt9EO5xBXSq6VvExQZarWSKc+hcMjFj+4dANOlMZFgk84l0nJkwYrOFRDuaDDK3rBTPPtnfu1zCqpEJR16FkFWPkesBzwUQNR/Swrt/Mh4cpqNql5WY3ZMIUq1DKydIOZtOWaqKasmoC9EyWUnnJPNmd2JgLX2PGoji/GNdey5ElaEqd0ryogJy168e3g9Is1Znv0mPX/rKybGm0q8rol157UeMaTE3Il7Fvhp+eO1EB3X/l//OEN71485X674U9PPubN6mqWjfc58DQu+Q/7H/GB39JpzdO4osvVIbD9iJWexrEE/fj55w0neY5IGzVwm1qL5SoHZ+kGHo8rfrp5wO3Y8PNfPuDyX9WcflhuZ/lghOJKFrkbB/xtD8OI7Hvyo8cvcC9//2N20UaKo2opWhzWSuYsqNbDHZzON4sTt+fc79i7tjDhFV0M3G5PSDctZ3XHj9ZPOKv3fKGnPM5r8uCJkunGCnV20qseJnFVK5w4lVfHZf45Y7tvePrJQ9JqxWLdc/nwhnbZc+r3vL/+ghPf8cnunP/p8Y/4crem6uHpzQWhh7+tEv9+OVJXCTdC2NpkoEAWnc2jUnnMMpkP2nvPjxxA+cTYiVpvuE+Ky+AQQjbxf3TQh0xysPOBx23Dzgf+6OQJ/6c3PuQfnDzhF5cn/KuTN/lof8KP6g3/65PPeLfZ8tN7Z/zLT9/l5/IANjvks8ew2f+W9v63Hy5CtVHclUJjZk5Tf5lp+u0YABa3Mgi+VyQVID75UHhItZoMPRZWLh0B8IlJnfqLp+tNAPSoX/zVLUR9ZTiHVDXiPampSU0gtxPAtoJDrmA4MTPCtBXc4BFxSMy4PuGSgYLZI2/eP2UHTpJ0sXgq39sxRQ/3urmq4srrhQN4d0f1ECngO1gyR6pN7hwXBuK0GMMgegDnjUdWS9wJ5o2wVcjftBz+OzCmqNCvSMvtoCg2l0699HJHP/3MOAY3z8Y7CcU8D6qTnvtvPWF1smd7veDzswu21y3tY/CbgXCbkNFZskpvbQVuyLiXDcKDmhT3PJNOMq42FnCOmFSoJXEZNrxeXfOYNV/0J2xTTZfM1Xdiv6VIxO+MAqRnYc6E0af7jBS2202FKjvxDWsrqu7wu24qlMOk5pFsRQJfeltD8XfxUqiPo+KXYM74wymWFXgLi1fLx/ar4+vULs8+V9fIvXNYtMQLz+b9iv7c0ywH1ve3NIuRN05v+d++/de8e7LhiTT8Lac8oQEHt9Jy27UHh/AcOA0db7bXrMPIG801/7D9lAUjv7xc8G/qSz5h8bUfO53WdH94j/HBCv9aYvnjAfewxz3aU9Uvnm2dc8JrhSSkXQCF8SwR14KuEherjnV1MGzLKgSXaevR3KtFkeVxdJ/t448+uYQPGtqrRGo8bjAVSeUTP14+5p8sP+T/Ff+EDz58QP1ZhR+EZmcqs3ENu7cSukq4JrFedyybgUdPT8g/XxC2QpWEsPeo82zymk/ePme3aPhyWDNeN7SPPb6zYmKuhOFU6H40cHH/ljokzpqORRjpUmAzNFx3LWPlLQ4ZYcXAshpY+459qiwTPCTG0SO7gNs5wg5TzbSv2AVzR+FUrpXJL8g5XDHkXC463mivuFz01BJRgW1uijFbxagBh91fPJlOKstdz4EuBbqxslSnIJy4jvv+ll2uGTXQa8CRqEuqhPlrjAQyI8V9PXsaGTnxHbVEdqnhF+M9drk2TCTQupFuVcNbl+TN+lvtjhcLwruO/PEnd54ToKpqmvXK4kbWS+JrZ8RV4Patisenaz7Iwu605h+svpidxyc5+j7VPIprblPLLtc8GVfsU2WO2i6VKkZ+xvDjm8d0oU7fH0C4Z59qRnUs/Mj9alOYecf10HK1W+C/qLn/7zfov/3zb3yPV4/ve/5QDvKzu1PIQaxxyAw3l/TWjdQSmLywY3ZmLNEvaMvPz6s9W9/gVdHkyNkRk8MlTyy56jMTPhUDlK9I4l+lMcbA1e2K7ukpJ3nH4nyPX0S8ZO7XGx7Wt/RjQBPshga3c+yeVLitI9dKXBog8T3UJTYDuQskJvmrelt8zoZs02uOx8y4FoZwLP8+ktXOvUwecpOJZ5ncZKql8vuLK/7Zyees6oG/kRMe94777Q1/cvYFf9he42+U/9/le+j52mC/fzUrtsaEA53CyOFCmFghZ9xO9saIk8qitSxMZ/A2HSM/r2sP73EMII++5h9ytK3p63diCOI94r25UYcpzxlArf/UW85zbAWJFp+YQzan9Gkcs+FfeYupr1vuFDnK2xfG/cCE6xETPrHi5SczcD92bp/Adp5c0KePMjm7B4fUwZhwMNXEC92Hr+h4LlCZ/3f4eXHMf64byK+S+R8dK98mFvf2nFxujHndnNF7Zwt0b5VlyfmIAWdmxl/mUFdcnltjzcRN7XBu1oI5ySzcwNp1bKUhY33YUf0BrHPEVN/dJUd92XLnnD3szmduUEc3PWtDs+2KTI9yAOI6TUd3paPuzo2rvKlMc4utw0P9ijPhv0nbiXewaGG9Il96hrcbugcBTjuWb2ZYO9bLa37/3mP+ePkln8clsR9YpgXb1PDZcMo2NvQpcD0u6JOZ3Ca9waOsfc+P6ydc+i2+PePP3Rq+AYRr7RnvLRjeWFPf71icddTrHhajGRe/4JHroozwVrzR6Kzw05gPSLWwfunKJ7LKbAAsxbzX+YQXZRFGvMvULrEMA8ElPr86QXKN7zN+8LZ9td+9DFteD1fmK3UVWH4mkMGPaoz8QtCTyPnllmUz8MbqhrOq48+z48ovzI+lFHFFYdhYlFaXK4veHdyBgWe6nmF5tuf9i8e0PnJZb1n4kUf9mr8dHzBGT++y9TMnix9snLG3Cz9SO2PCUUEGsULyWOap6hW6YI6uj6n1SKfnJ7WID0gIhCCsw8CZ382/dsyCR3UEAUeaSdOJCU+FCR+zL0XLyNINJByS9GDGJge5ezPlipeWn14DTsyKLUhiVMejuOJJXNM4c1NvXMRXDlkv0PMfAAj/2qEl5xMQ7/HXHW6oWCwd7WeBLq34ZPRc3188l7l2KJWLVGqVoqSWHR0kzeD7GFh/5fe/4WeeA5gnQy8ZjxmuOZRKEjF7rnYLbm4X1DtBxvR3ZhFlfeEFiGcpplOlNy0LTsyltfIJ75SEp9fAqH6uXpKF3HvyPoAXKk0lUz3h1PZ7TsIYPTglOjOfmcbkRD1J31/VIU5xTcItI9Jkklg/fZ8rNqmljont0JK3AX/jIArUGRUl1InlyYCvE24QQmN9ysCBjRCMtSuAL3sOLN4EAjlaWk1AT23uc/Eo3mbOD3d0zhPFemJkEPLgGJvA592CD8cVHw9Lngwtt33LZxr5C3+P66Hl5zdnbG4Vd7uHXQ/pVbInPBp6kKmSdE6weh7BxDPPHx+T4x7iSY7uxgLU3GEDx8kNx+vgg9v23edf/XEA0HL8z/L3Z19UHeV8no/H151OcvSYjwB0eU59WR870Hx3f+oR0z0b6j3DGJp81ljwuDDpcK6N0dMj9ckM+BG0CmiT0SN29+/Hrzmela/DYTE3g8jnzwuTw74bIQ2e211LbpTt2KCNUq1HQjviNEKMqDpTNyxMzWKKlpd73xKn+CoTmkioEsHbIjHJ5I9i53MlqSwcLTIyZfPS0Qkcf+0b2M8mIG6dG8dInONL8s64s5tLUXxS8kyF4CzCkD1drBiDQ1SP+jnLW4j1+TpRks+kStGkaKUv3Yz+1x7PMe38ynhuS4RY5J4T0sIxXnjSwrE+cbz21jWrkxv254Grtxr2Zx6/SHZOVgkcfBZX1H3kKjdcJQN7g3ocevAEiJ79WNFJIGbzKLodFvxk85A2JT5+0rAdS969EwjBer9rx7j25NqxeCvw+rtPWLx1Te+F20dC/9TDFxUaVnBRw/ji2sqqjR3o1FrE6qy8GGzRMmrFk2rJbqgIPrOsRqpyLdTFmXQ71nx6e8IwBELINNVIHRJx8PQXws2PKvoLIa0SrsoMMfCvn77Pz/YP+B++eJdq43CDsdX9hZAqGM6VZjVw0vbmXO7suqt8Ii4zw4k3NeIO3Kj4vfA3Tx6y8H/CL24v0UoZzqyIN65N7TZcZGoVHncrAD7dneJEGZNnzI4qJNoQOa06zqs9wSWu45JdangyrszDR8CHzLjIRBx+P7VpvUL60On6gPl6mgkIQBvPeK9Blw16b2DVRi58V+ThBq4nXAZw8Dk/3BNrF0leOKl6QvEAAEtgyirFWDsVxa0ZFQtKFje76vgSZdZKZOVGVjJyk5Tt7YIn3Zo6RIZFoA6RzbBgUP+t71svBYRrSrDfo+KQ/R653SBVYH19gRsv6c4913+w4qN3L/jfnMQZNOdS8TVDtwje2GqToetsWT+N/MxeeBZ8+6NV27E7uvVeWN9VnwNZHcFllt5ywrep5urxmvBFRfslyP4VMpr6FmOaVCeH9KSOnIyFkigwOuLoyR6CDLR+ZFGN1D7Ra8VtWrDL9byP8+hItxXjkwZRz/LByKnbs5SekE22mwfPbl/jYqDSTComIpPEyEv+5oXFKzCkyvizgepehzQjg3O4GLiRlk+Gc2615cvNOemLlupRQFslnSdyoywXe946v+Kk7ecoPQMtVhWe9s10CRiOnngI7igIJoUBcMeYYuoPn8x3VIVdNuf2XapINxXDL5fEG88+t/z5Wxc8WSof70/5yc0ln+xPeOpXfHp7xtKPDB8Lm08T7tMnMAzo8GpKcEVBRsWPSo5aDMgPPM88njEsmoD3xJBOgG6SlftBcT0z4Jt+R8tdOU89xtOm/SE+6Qe7UP02Q7EebqWwkZT9ZgArN0JuSt/o3l7jYmErv24tPEvK76KJXJhrsIi/yYhlHk7IlZBrd+eYkbEFA0Uef2qO26lVxjO1Ht7OfqYl6mwCf6IeXbbkGEyZcvMKMRfPG78Nc7lZvs4BDE0fh+nwHq3mpp8lcJ0S9jDcVnz65ByJxljISWJxsqW+jTg66CJ5JQwrGC891a0SbjIyvFxdm/NKtRjR9UDbDCwqY8Mmpjllh1NlMZncMqLZzE/H7I9A+NffJCYG277HCltTBUym+z98441GjUHU8r2K2hoNx3asGXvhnq9wqjQyUkuc53HvMnWIBKcMCYZVInlBFxm+h/7j32g8D2wfOYeTFU3xm4H48fAet2iREBjeqrn950u6NyvOz57yZ+/8lB+fXtFVnptFS18FdtQ8kjU7qcFl/kP/kL8ZLolIASI27wSX8TLQjYFtV/OkW1LlRLesGGvP49tT/s1HD7i5WZJ+2jFsboAeQsCtVkhdM15W7H5/xXBZ89qbT/hv//lP+P03n/LBL8757/779/jwl+do16DtGn0P5HaHfPpi2spWn9h+6i5Mrq1eYXT4jfVT5+DpNoEuKKwiDx9es6yGmfFufOSvnz5k8+EZzWPHWCndwu7BeGX33sju9xWprMc+hMR+X/Pv/vx9XOeobh2rT5V6o+weCJv3IvVlx6oZeef8ivN6X/CH4YaTuufz+z1dU+FvAmFnBmvtI+Hpf7rHv/z4DLwiy0g+HXAu4+tkxabsiNHx8aNz4ujRTcANjrxKnD+85XK146LZ8ePlY+5XtzyNK36xv+R6KG7fMRB8YtEM5AshnTp6bRnWjmrzisnRZzMpV9JMyk1EIa4r9n9wzvjamjffG3l43vNedcU2V3yRluxm221jt49HkMzCDyhC6yKNi4zZc6/aIg46rYg4gjP3c8SyyHO5niq1MG0RpZWRShKnrueh33Pqep4OZzz69IKfPnmdsBhZXO4Ji5G8aUi5Mf+XbzFeEhOuMxOuI9CZmbtPicWiprpp6S4XbEqlbpKHT2OKGpvC17NYL/KxBN3gg/vK703DfWV2znNVZQL6WdwM3D0Zj1KLHUjZe6pbIez01YhaekHjGJBNzs2aMaa6sKXBZerChEdMxmGhbwcmXCcmfOWoNNO4SM3EhBuzrtGorlgdJHWg85z4DOR59cYRE47P5IkJTxatoCLsxoa8C/hbT5ZkDMEqEZYjJ6c7LhfWCJQLvT21ATx7fmuR5Uw1wun1oCXF6QC+kUMm49SDnzFDixBbtjtPjEoahDwocivEU88XfUsaV3wxLHk6tNz2DRupeTQsEVGa256z2xuaTYemiL6yTLixmkwy5rLWf+4abN7PHID4EYM9Y/QCxF20fnKZFr3HaobjGKXJlOkZdvx3Y+jhsRSWFCk92eaMPjHhJv3XgyHb10iQJxn5QZ2gKHqQkbvD6+YvjoodgfnaOZbdUkB7aoS4kJkFNyZcSz95WWRkipRZIHhyI0gXjmLUXsHx23R5nxdvB2f1uz8/onDn5w5MeBw8231DCjV1HVmtOuo64tuEECEmVHNROJR2H357TLivEr5KBJ8JLuFL0st0m6hIBwmlWtvXcYzobHP3DJA+aAj0K08eCwym97ozntnW7JR+dO/KGBOuyZg+OBAmk4mroLPrtQsZQkZrRYP+9u9vxRj2jsLiyDnc/Ap+/fNfXAHwdU0+beh/vKJ7v8ad3fDaj6557/Qzeg3cqJl7XcUVqRckKUkdX8QlUR1edC5kTOtf54wJHKJnP1T0VbAoJXXcDi0/e/qQzx6f0z694XzsaekQ55C6RtoGPW0Y3zihf62hefOa9995yj9+42PCk8S/e/wO8lNvcudFQM+93Z/D1QvZzfXGrqvYOiumFibcjcUAzdn8qE4YxTMmVzyIEqswGOBSob4WFl9oMbezJITxLBMedtw73ZKyYywtj/tNQ/tZoLkCN6h5vYwKDvzZwI/vP2ERRh60GxZ+IKsrxJxQ+8Ry2bMH0mhmMC4q1Q7ax0LaVownGffmnpP1nkUVebi8ZV31fL475YNHl4x7M21rHhuT3V8K6YHQ+MgyDJyFHZd+wy413I4NT7rV3JrrRKlDQtvBUnWWNbk2M9NXcgjWxjJdbyi5dgz3WobXl+i9mmUTOXcdDuVpbpGSVBVgJkGmopTIgQn3mnGVxdkt/YgIJPUoDo+iYvHHBsLtuXyEMCblTiORpUTWMlIl2G1anl6t8eNI03q8jIQx0KjnW2LwlwTC4U7l+is3r+L6OyZPpxVjaQp6Flg8mwf+7PAFWH+XcRxBdnhfgVQm89In9ndlHJjUA6CYKuU5GzifMtsVy+rbpZo+e9K0IMjWxxx2FLOKsnELqkaqUv12tthNCPsU2MaaLlXzdl71EVw2k5HGUbnEshqofOIkdFyELSehI0lFE5OZPI1AsoKHZrtZ5CKpmaQ1TsCLnxdUU5TbxH5/FYRz9OzEmBT5jbMeK1M/yNxfU/vICmFwjqiZnC1fvBGL02i9mYis6sEYDm/VX98mQuvQtoFRYBh/+AWsKb7omXuUqKKqaBZi9KQRYvRoktmMyDLCmSPG7PeYwfuxzFyOzNoQTJpeAJ4G7FrImLnbEfj/nRsCiLOFapG22v4zVg2VQ4/8tI/Hwk4qIEUdMPVwc/depaXnTKRsv2zDpanFgDnubWbP1Z6bnaCPN1w+szotx64c/0j5SkhUMwURdzB281NRYboGX9HxPLnty37/Ixf1Z58H0OChrSF4ZLEgDA42CTdEpO9IdSSswN0XdFmTt4lxKeT7mfG8IZeMdzRBP8Du5SrfnKj1gIZI42OJ5ynmU0enYi6LyCkOLGZzBdZn58sjV+np38dM+Pz08euVUui4C+R1YtiPndcFyw2fmHSMrZcCfI4/j5SClnf2N1Y+gYe+jsbuhoS43+KNbi4ycfd+W9xoDYDn557/WnnyqoEqkCslLa1FZb1IvHG+Yd0mtm82nL3Wsz1tuFxsudWWj8dzBg1sc10cthui+lK0SBatxGG+Lkei9L7a+XLR7vGinDYdo3puYsvoHKenOxRYjjc8+LMblq9voB7IK0WrmtvzJePrFXqujDnz4d8sqD845ec/W7B9lKHrkejN9ycmZIgvLNlh8lpQD2mdOL3Ysd/XjNqQG4fE0vs8mDJps2t5HBKNtxbRNozc7ht8J4ROGSrLCI8rJa8TJ81IG+Lszg/Qj4HUNOY7Ei2u0CXwnZKfNnzgL2mbkXjuuGy25OK0nVW46Vt2u4a0qfC9zTuxNUm4FXatyOt8pg52bk+knojifcZVidw5fCdUG8i1sLlZ8JnLpOx4o71h6Qa+GE/4eHPG09slVZU4WxwM6tpgnlm3zZL8qvnsPIMDNd+9niRZrJxuMm6vpCSMKiSYTdjQA8FqsWGlVdIpEqxvPKmjz56Io3XRClVqTcYwqaTVIpAFGplSnWJxXXckhF4917mxZC7vOD3d8kZ6SnJC3wmpr9AnijzZI0++3XXxckC4mPmOhblmk6c/exPLsI8VV2nFbW7twzk7iVMBH0UsYC9XMUb8men4uwLxuV/gCIxHNfMW35mEdHbyK3/b9HcBv73Fyfc4pniwmYnDADjRoZXgSy+GItykBV2uuI0wll3hBqivQFZQNyXnGKyXuUnIItrmna22ozquhiWDmNPrkAK/CxFlbRhZrW6oT3d4sUxIL8p52PJe84jzsKPxykf921xvFCroe0ceIcUS4aZmNrGNNWP2c7zCtC5OZcKQaeIpi4mMcGzYA9Okbs8Esd6ZILmAfD9XX0+qgdOqY1spYz4hjlClxEnJfx1rx/3lBjw03vqaGhfpz2tuz1f05zXs9tD1MP7AJenPYVenPnlJSo5C11X0u4AOjhydgexozJmLzHnEgLmlTw7okwGNs9eomDRa1BYBgv07tRZ7Je7Q9y8jv5P3FhAzKPLWo+giMOTZfRzhToHDjRm3G/GbEQ2e3AQDXXCXpS2AVxIHt3MtMTOjPbrRMsJdxjLCPebWXo61GYIVoHG066fjliusWDIIbhTcLuH3Cb/PpMqjBPs7KiFXjlwDQeZYnVd2/LbPw2Np+jSOP1Nbo6/fg9UCV3varTNX+n6PXj9F+z353pL0Dy7Jlwtip6T7GdaQW0dcW0qHpoje3MLj3Uv987zLrOoBbYVV6GmDLQ6DhDvF1qiOUS1juIsVXawY0iHCVeb/ld+YLw+dC0zH484edbZAPXZkByBNHjHl9W4CGeUSyeYaPEZPEhjiQdUmoibRdeYhs6wssrZyiaSOUCeqxYj/HkzAfq1xbBo17ekjWbqmeGDAn3MN5FVD/PED8tmKeK7s3k3EM+Xi5DH/9M1f8P7JEzZNw2enp2yahizCZ/mMT7YXM2hIOrG9xfXZJVpn83JUR6/VHMHUpYqojspn3jt7DGprp73W3O5bvM+89daXvPuGcv7+hnf/7DPOhg1RKvZ+QZSKLzglC3zJCZtfRP67/+sl/+6XDbt9y5dPE+xvkcrju8YiJHd7dHwxhXTf2z7MlXDxxg3/1dv/mU+7M/7z6QM2+4b9dUv4sKa+BkmO/aLli8HjvPJ4saTyid2TJedX0NxkxqVneJBYPdzSVCMPVltO645VGHijuWbte/61f5+/+mRF2Pq5uO0GpblS1j/1xM/WbM8yP/uxZ3Pa3GmB/PJ2Rf6yNVPcaPFl/aW1NaUSraeVsmhGzpqOuoDwqb21rUdUhe62or6G5RcJ3zvSouHpLrA5azlr9ngyP7l9wGcfXVI9CWzOExfv7XlzdT3Hl1WSebpbkOvm1fEYKfFjUq4pnQiZo+vJj0r7NFH7THWqjL2wU8+gQpBEy3BHFWtlKlvbBlGa8JQgyqiOXTb5+aCBTW7YZTuevoD5KXrMY+0yp66jdiP7XDMkM4DbaMNH0RPIXNcV77z9BacPNjy9WvPBz17j+moFH++QnzzBf/zt5onvH4RPIFUc4h2a4I53+FQBUZMO7HLNmAOpVCwmaXo6miIS7iuS9ckZD14MI34sZc9qhmS2wH7mBlxYHM0FiP82WYIXPSaJrH17FF1mwFy0RAkVt0qwWLkojj4rWY0SnJjwvLPHuYtAFAn2dVytTwhdChCnAvQhUutVHkGMCV/UcW6BcKKchI7zsOUybLmWU5oY8QPEEUiFBc+ClmJUVFdcNG1RNoHwrFJyEWU+Jgd5+TEIl1k+OC3qqqng5fLsRJpUaHxi6a0PK7kRr9n6mTVTS6QV61dcViNLte/P6z2tH9k2sG9P6dsaYnylZbgTEDcm3DFGIDkzKyytGq6A8WPWejKGkmwEggjgj5hwNzGzzAx6ru1RsliPdJIiv/4t/fHf45A7vWFHANhR1AcwufdLkXdLTIc+3dbMHo8V7dPD5HJup/tBlTM7X0+qpnIscDIrmu1eo3YPfPa0tY96MGBLJQ4qgoxqTH1yxqLP8vdSFHiFr4Ef1PimOTZ4WC3gfG0FrlGRIcNNRH65R263jK859ieCJgPceQm6NsWC1uWY5VyY8O7l/V3YWsb7hIRoqqLiVzPJueGYCbevWDJxUz6iJZ4F4NP9vgDw552Jc1+4HpjtKdf7YOBG+bltS0RLDKM9p1ikqKWeHNYQ5RKzFqoCxGsfyQp1Fc3Bvkq/He+XOwCcw757lg3/2h4kMQb8bEV+cMJ4X+l/LzHcz/iza9740YbfO3vEbW5YxIFNbngaV/ysu8/VuDpStzlaP3LhdgSJVJJZuJHaRUYN5ClLe3KAVk/j4uzUfBtbPu0aNrFhHXrun25Y+YHLsOGd+pZLv6XXwEYH214Hq5sdN11tTPjfLuA/esQ7pM6I7yGaOYmEgA6RnJ+jRPk2u7ww6urg3mrHP1x+zFnY06XAo2rNx0NAck3ozDjN7xzJVeQqswcGn5G9MwPFLiPqkGXktdNbIwPqjtpFzqo9bzVPuec3/LR9wF/WFh+p1imBZCX0SvtESVsgO7qHNdu2Zkr+ARj6irATqq3M7WIplHtFKPd6rwSfaX0siQZ6iPL1maqKdGJF9/o2kysh3DrUecZQcTO0bNqGm77F33jqp1bAndaJjYss/UAliVUzsJ8SOV6RMc/3X6MomZjwvP0qEy5kAlKUDcZpW5egWT62zqTrrURGdWy1YlTHbV6wzQ2jBjxpBuBeMrUkPLaWbdxIIyNJrKifVRjwDMn+PTrH6cmOxbpHevikew19WsETkCcd7unmW+2T7x+Eq00XqBgA1wO41ZSRbsR7j+8WdGOgzxV9cSaagF1GyOoMnH8DuD4G4l83ngfQs5or3nEV8phhz9MiMJp05RvlOL8rABxA1KRQkxTOlTgmwY5nVg7xZSZdJpuj9qjZpFsoEpWwjcj1SNhGXLLuCzcJpp9TkffOLpKpT2N2pH7Fx+T0DmUihVn+kotJxNRL6nJxhXfZ9j1lH6iUBY3eAeFg14yth0zJMTMUyhGY0MMaq4xpG5PcbZp4ponEieLUDLH8aH1UU53KSbY4jWCxGpUrNzqvaO3ITYDBv7IgfHKa1yAQFB+UEDKaIXmH5sJiK1ZUykDN7KGQR2Nk1ZXcaH8AmIKSS284yXqRU3tYJGijhwifVkgDUMGxIaqqImQzN3vVhl3sFl9XDM2YIsOOXmPMs+0X68FwqCs7QZ/Z3vSgR+e4HH42fZ+DXQQOheHod0tig7pDXJmIXTOqdyXlthgrP6sgN47cYLFkWew6sRVEkaTzlWvv78evOb6hwJ1aT7xsya2HkwX5zQrWguuV+lpxnZKDI91fkE896WKJ1qXtbUj4TYSU0ODIy4AGR7XNyJgNjL/EMRmXeZdsLVLArsNk3MFlasmsXOZEMgtAVIgF9NpG4Jj9tqeOKlQGqwswPx4HVdXkBwOHx4OM6tmLq4Dxcm1oNqWiFY0tUighsxR9Kiz4IrWf5sTfWvrJUZuFHj/3PMa79cRzO9eaauR0vaepRuRiS3jvc9zZNbszx5NLz37tWLc9V7rgl8MFW615FFfstWKfa2qXOPFdaf2y3t/aHdJjghyKEgb1Dw2CExmS1LFLpozrc8CL0vpoLQ3lr9mnmk/6c57KkrgRuk+EuBEejRXDNsHQkz9RxtOW/HsNqzby4GzPoolEV7MLSnQV8SrTf1CTrr+7DHpcFbBTwWao+bC/z5fDCbtYE3MpNjTKuLJ50XeCZI96R+48ySthZwqj/tyTWkFHx5PtEucyT8MCJ8qjpiNmz716wy+357idw/f2GfpzYVwFYisMZzbPxoWi0XF9u5jXwCKmRpRWGUrNRY8vtblIbKlBsVRfhxxwomzHulyfFsM2ngr73jMuSzxsD24T+ODRJVfdgifXKyRbf3uuDkkCx8O74l/RvoTZ5M6N5Gve75tSA+aXfPNaZbnsOXv3Ee1bN7zx9iPCItJrYMDTl1ZlRyaU9CrlAMIHLTniEohqUvKEkVZGej1j+n1UYFeKCXVp7zn0h+vcBtJK5sT1OAT8kp/kbAax+bsRhC/PmC1NmsxD9UO7Hnlyhd82tNdrvtw3PBrXs5wqFKZwVD+7bT/rgP7s+CYgfsxuTyMjdLliFJMCTa+pSoUEDCS5kdKfogc5uhRmQxzicmH5X/0xq8/F+pidT2TvrG87GBC3s9+hSeY+5czBxGKMI1Gt4dL3ifbzDnaOxbqj6g/mIjIB/bIwQA0AVi7R+GhmM8lY3Fd94TqB3Okc7bMFLHRikSNRbRHlBpNq+WgLMVcp4m2hn4vZ2pRxaBgmM5lZGViXAzhHUZHZAfLACdqYridfQLuffq9Em9UFUHtJhJypuky1TYRO50p2LYmzam8LREm03mJz9pWS14HxvEbygAuOry+h/UDGc5xxLefTkVuHNomqjUiTSJLpoyMi5ErRxZHr/HSriYLsrb/NQLgaE+4UmfofVdBoxZeJYdWJbS2V7qzCcOrJTqi2jnpaA6ndj+4sHF+lIQ6pAtLUUBmrfTCfKwW+OTdZyK0UMxo9gPbnjTyB6UnyMX2VH3vQxlhEu6/nWaEz96SHAsSf6TnPoVxLGQjmjK5BSUuI68DYK6kx5VTYW8a8irUYOC9f/5n/fnz9cO5wXT4HFMfLls0/e8jwxhJtPPk0oI2n/TJx9p8S1XUmLj27t+8TF2KRUaE2pnw70vzyBn/To4uKfLFG2wr3ZMTtxtlU9mUNL0rjIpXHcmgLc+MkU/tImz3rEHngE2+GxJdiyot+DHNqw7M938+C24nVFgWVaaHJLFW3XX0E6o9amWYfpQkclksMp1ZcFPMxEbXorEFDcSb21gseInVINM6+knN3et5/a1fHfO8sFeuvuZfG85btP37I8NqKBxfXvPn+R9w/37Fqbnl4+hGrJnHlWj6sLrj2LZVL/CLd49PteZHJ1kR11C5y4nsuwq7AaplrAU4mq+GyXihkx9R+5srrM0KXKnaxRpn8BCKtN/bcl3itp3HJz7p79KlCfzLAv9jBz0aGHLhNA+RrxrMlt+9eMvyjBSfnj/mj9z7lnfPHXOcVH8X73OQl3Qc1T/8/J3Qf1t95d+/vTUAbnt4u+f8+fo8xe3ZjRUwecZnx1IrQvheap7Y2sghJ5nliOIXhTIgLkL3n6su1tQ1ku0d/skh8fHHGSdvz2ZNTmieO+lqJS2HzroHuvEhUFx1tO5L2NTxtyE8XqIMYtMzFip6PcD8fGRIK2nmqK4/rrM98TJ59rEjZ0SdPyo6YHN1QkZKDOrN7Q+nPXUlJgfrGGPb05IQrf2LrAwdxpWhrrYuTchIoWEVJC+hPv8dV1XSxu8kMU47IlKOLdcJ4h+rdV7f1K9cqyuW9G/75/+oj3vgvenybqC/62bBwm+3RY5nex33hqkJPxS1tWTocinqxJGqJ60sbjzfMcoScFWO9VWEgFIJMcExkF5y7gfernguX+Iuq4q/SyKd9aVH+Duuul2fMNjPiR394Sui+gxjxXSZGzz7Vd9g34BvZ78xX+8J/1fCSyXqo5BnT7u/8/Lly9GSxOF9ZBDi5o7D/XRk2GegsyZFiPHGggIwJL8TRHRlz0kM/mUTFbyOid5nwuTeNuyTHxMR6MQfx6dlX3BsdYK6qUSrY05epMYpkPHGosE2V2CMmfALZ035yZV8qmMujcjhmHCr7qgemmwLaeWZ7U//rZLQ3JRM4Dky4G7UYW9l2Xam8Z4bZtdUfMeGpCbja4V4F8PE1zrjqxZjTAC4oISSb5F0G56wwNRWojjc3FjQ9Ar7I1jyIz0iTrRUjCzkdldbvbMAW07k2hjWNgq+YGSfgO00Av/VR+sSMCfdFPn6Y3GcsXqLFrG9bDl8wUURHv1CUBs+w6XeGg1x+38UJGJffKIkNcyuOs885t+NMMvRSi1RvIFzrcpxq0EBhwpnB/SRJ//vxG44jR3Sd/v3MOZ9bz/DGku69UyuItOZWL4MpFUgZQiDeWzBcVLjRYstktAJMuOqonuwtSk5adOlhm2HMzwX93+ufS+kJdiXeiEmVZADdu0ztJiZcact8nLKbFWO/lqRbn1VlHKuhSgHs2XvSTP89Z/sTOIfDuqCwgrEovcRZm5QX6w0/ZsJ/MDO8zv977sitZ3htRf+jU3g4cPKncO9B5NzteLd6wpnf8yStiL2yTCu6XHEVF/SjmczGUnw/9R2X1Za1v9vuMLk23933hxui02klYPs5qqNP5ore+EgbIvUE2srrulTx6XDG9bBEHnWEvxLcn0/vG4FE/r2W4R8t2L93ijy45f4fd7xz/4ZFzFx3azMidR7/5zXyePmdd3NclvVHgHEIPN6tit9AIV0EtMnF5A/8Xqg3RXXk7VRMC6E/h7RQsi/zbfZm6jaYAi12jlu/YBgD6aY2b6ce4hLG04ycDywWI+9cXHHR7vjZ1T0efbGgunVkr2hlheG0zPjTxGLZmwIhuQL+alT8rHxLydpCxmyFsZiNAU/JkZNDvBJPEqk1ib3vrRAsGeqyropLobvU4iOisyJmIgYzgnfZDACb7+mqOTIpnGXkghXOp6pdWStpzkjOh4Lc161JfsVaZbHsefPtR7z/B7f0GrjOrTHhGuizPU7krKrM62Fjsst9ZpL/i+lLoTjLk4i4wna7uehltzojE5PogQmfmXLDK61kHvqB18PAF25gkdPBA+gHz4QDz0VbYBIw7ATUXIzX1NT+Uz9FnmihMo7zwb8OgH9dPNnEQpppVek3PwL5x7FnU+/BPF7hte6vPaaFphzXzvXOBCtOcd4czZ3P85o5Zceur62Hohcs1Bdjo2KCIVpv8CTn/8r+LGXg55zRx+vsV3cU+TiQn5EKTnIYu5FnM6BKBrAnORQcFkUzYObgnHqYmo+H3HnymA2f3lMx6eCx/NAO92FhJGXyd2PG9QkGLUZxZmCRShHh+HOoYPFSAcQ/H2P+4MZz3NElW5+vHzLaQ9550g5y73E7R+gLIAz5LjBUDMyNzA7cU3+AJGftBs4AnxQQrgBF3mz95PZ615eJJhQW91XYl7/GUCcWgVMHe/SuKAEmKbi9buoJv2MFMp9oE9v97PmvpZe/MOLPWwBI2VA2ZYftf7l7s5kusKPP41IBcAI5ik1P6XibpX5S7nW5LuxNLfgfvBzkBzaOHdGdg7oCZ6qIeAHaCvGNBu6BW1pvcQ7l/tMI/aVHkhCXAfXFyX4ujGC57k0FbYY62PEr58tLvcxcOcnc4QQRDLBO0TtLX5zaRfkyLfhwPOGzcck+VmYSWYq203JpWqh+ZWKYT/HD/X16PXfmgWeuqLkqNu0ZPZzv07ytgpb7liZrI+xyRcLdyXieMsPthROhIgfVg32gb707v2kcO+jnqsQNBlvbUKmx+nL4m7zkuT/fvaH4t7fEB5mTsx3Zwy7VoBA0c+WWsxHUbHBKMYESmdeYrR8sLqnMsrEAg7JX5zHtaSOLDqq2Se02seNTKGxUZ7Ltsg+n/fywvuXc73APBvyfbpDVMEeTOpTtm5kv3lmyvTdwdrJjKzWfjafcpkWR9CraQn/fsdt8dzl62NlfOa7tXPEuQ3YMyYBUih7XWZSXKAxnEFdlPoy2kzTYtew7QYsrul9G0uBKFreQF5mqjtRVpF9GhjOTl40noItEVSfaeuSk7risd3xWnaJVNmPQ6VLMVkC/d77h7ZMrNmPD492KbgyMlYHhXENulLYeOW06+hjseBRpfS5gedvV7AYHyR+Ub6VVLdVFeRXK36ZAFJ7sl3wULjipOt5srjkLOwPmg1DtXvA18hzTy0lGbguTfDc69eg15ebx3M1q5dFlY4+VFUFyBbWPrJueyicWfyjoiWdQT5cD29ywzxUJz1jk5ZMyxJbLE2aU2YQtqcOpkkoR6mDkVq7lgh+NYLLPlpF5+6OGWU0tWOHDCoWJSKLXxFAlxrNEvB+RfUa/A5J+uSnvxy7iAJpN6lVAhyahT9ZD0biIk1R2irlAH+88eD4Af1aKPr1mknT5KSuoPE47P2u5OYoB8WMwbp/V2JXfhmfIyx1SEBf2JTMRYbOBMyO10ERcsJub8yWrcvQ8vlmz6Rrqmy0n45aaAXIyZ+ycD49lTOvow0LgcNEcXjMBxJe4G76HIZTCTgG0U188wtwTrgl8l/G7SOhtgsyVxVtYhfwgNzw+/ycZ4vHQ4jKlU1UP5jrH9POJiReU5Nwsf/Oln3xiLRwZlxJhNxBuKthG+tHiVLocGIucHjJBpiqikGshNQ4qudPH/IMdz3NHT0rYJ3STSAuIT2r64oodbjwyiPUFB1cc0A/V0extYs3TtVRc0V0WXHIG8LPciTGb+sbdCKEzsGfAwiR3oeG4JvlqD+/IixrWLbnxFg81SdILGAdMiTSxyvPh0bl/29hyOTq5ywImK5IyEotZ2lz/k7uFxZSRMUFwNqEe/0xBVWeTN1RwPQSEnEthREEithgplShJan1nHsalqSnCTgjB0tf+fvwGY3JEryvk/BRpG+Ibwu6fOMbXhbzwcKlUbWdM1BggOeKJ4/a9ht1rVtByoyPsj+6bDrT26MmSTF3ce50x5ym/vMK7E8QHe/9gRrZg99/WjSz8OKuTVrlHXOYvhws+jCt+sbvgyX7B2Ad8yITWHMZVrT81FwnHfFuTg+fHMdAEmxNyYSIRvhoXVhQimovkfSrqlteKgEZgdGh0xLZiG1tu8pKBwKoeuC9bk9YXM1A5et+MgPOWq30scX3Ro63RN+6j6wXjqbB50zOeCDQZdzpCnXGuxEqJsggjp+2ONoyERaK59wV+AaFKjLXj8bjmkQof6D10YuKcbSOrEFzGq+IlzVLaShLBGQk1qmefa0Z1B2As02p2Ate+OKMb3PalbU+ykp3Ma4qhzMc2j1cIyqnf84/WH3PiOuSPM+5yRLZmTLUu/edX9Zqfn+y4rlfg4As54YvtyUzKeMnkE7j5o8Dj+99djr54Uto9l569WvRWFwMpV4yjJ+0C7ZWjuoVxDbt3I7KMaO/x1wE/gBuEsAO3M1B3/41r/tH9T/l0d8rPv7zHsKsITeL8ZD9HqF5lYbjvocmszvcsm4HLxY73Vo95o77mybDkw+Y+ufK2/3OZDhaR//rNv+G/Pf0P/GX/Nv/PR3/Cx5szchZ2S3Pi1lXk4XrDu8un9DlwGxu6WLEMA5f1joUf+OnmPv+xf4s0OnLQueVpPIHujZFwMhK7gLsO+F6QzvH5k1Nuu4bXT275/eUX/Lh+ZGk2O1g8eoEy3OO2n+O10LGM/Fv2hOuyIb37gHy6ZDxXtj9ShlO4XG55+/JLzhcjFxeQXjc3801u+WI8ZZsbW4uS53tVJQcwnphie0MxYPNz0WtSD9QuWh+5mJR9urYmnJHUMRJQtWsxHcVk1xJLe/JApwM3eWDT9nTvDAyrARjJy2+vlnqJTPixi/jBmn7qFZdsGbyjOqpJclFuZtP3iMMVuuE3AeDTmMD7V+ToakB8AviTBPfYgX0q2L7ySPDXGFO7CxwY1nlVWyZt5xUfTHI8ycpzduz7itt9y7IfWOa5HG9MuIg9Hhvb3UHhdytWd15zZzp6dcd04U/yu2mYjLDIsKIeMeG5MOG27yeZHzxbqHj+0Dtf8pV9O1XWp+q6FgpWOPSfSVlkzUz4kJBRSckV8G0tCHpHxlMqlVNP7eQ8/SoOtfYAhoz0jrx3pJ03ILYrLLWTg+latkq9ZKCCLIoUx/PJ20ASSIk0m53Vs4F2StyJG8BvFDeYYUw6YZazv7L78tkhWPRNbWZYJtk+Ol+me5FObLgcCqHHJ/cMCJ4ZEws+M+FH5/+dbU9zkE7EnL1E715bk0JBipmeeuYs9wMTbp/Fphu1VoZKSJXgqr+XpH/roWpgtW2Q5RK9FMb3Hf2PpXQ1ZBwRHX2R4Fi+8HDhkJUQ9tA8MSnq7LkgoN6hdQXtEQM9nQcvbb43Bli8v6PEOGbC1cHCW1wVojxKLU9Sw+fxwIQ7V4qoJeUiZ89Uip1VONM7Hl1IcjS3zAVbed4VpfM1N/eeg80ZM6AvTHgUcipJHiXxpnKJRRjnOQUOrVSHKNTChn+fbQDBo2tz0E+XjuEdT3/hkEVCLgdkkXAuU/mMc5mm9vjlQFNnGhc5DXtqF0u0WGCXa4Yc2MSGIXtLCal2tBJLsd2K2pVLLNx4iMEtRfKswpCtf968Xg4+BNN6YWL9cpnFpyL+tG4tZZUyF7vye2YBfuZ3PKg3vFZdIwuQ+zbHtxI5dz2NJJ6kSBornqTeXNaHMzZjS+0iq9BTSyI30N9z7OoXwYQXMm0oBSdnICtnO29IVuz0ewPY7mTkweUtN7uWfV6ie4cXqG7F4s4UHqw2/On6Y1Z+4PFuxXUW6iayrEZW1YCqMJwGhiYQqsS67WlDZF1Zb/5l2HASeiRka32aAHi2NrQ/XnzC/7JRKvmQf1u/xyO/JvgMVTaVU505rTvOqx19NnhVu8h5teed1loVtqnhr0IizcSWFQNTo9TnPa+d3/J4s2S/XUPpM4/7wFaFm8bUE+duZyBzgLB7QSD82baf43vf133/GwytPPl0Sb5/QryvdO8p/X0lrjPNG56TNbRe0Low4RrYFTDuJdPKSCDbf9N6FZlbOScm3MzwsrHmWhQoWlqcsaizUK6XqUA5M+F6yB23XWLtM0EyIpmohQkPiXiaSJLIp9nUM99yvFQmXL/OVVxLxVllbpg3aZKNYzA+MdrTmNzOjwH414FvMMOLrG5+/hiMg1VEjlnwyTAEOFr8PVsd/t0B5jrfGMSSg1xGXGIk4JLdEMQdSaFK0SJIthN9tLxkGeWAFLJCjCiKxlTWxCVk4A7TXiC2cLi4jkDinerA78CY2OxpSs2IXSNDRPoRGY0x8w4D4t/wx0/FkhlmqBz1fx+VMKZCxwQmOID5CahPUp+v1JwKoJFkeVtzR85EKsrhc8wyITk6p17RoQHi2pHPHXFpi/upryzszURP5cgvIR/c43MFfhC0MjbBChIG4nyvh6zSAsY1QEzFxGvKsy51K/WFJa8F2gppG3NuPm7xeMXG3AM+Md+uKCg8pMb2Q2pk7sNGMwwjdCNUHq0dGu6CJz0+GZ89744KWFPf4VzBz3Y+a3HCV2fRcG60/Z5L5rd65kKK5Y3b+0nWWfmAMBcUzOF22sZzPtMPaXyDA/n38l7P/nt+Tg2EKXYDbEw6zEUNPxL0IqEPHbn2pOhtAe/KQr7LtB+N6DXIKLi9Q0bBD1DdKH4AQkCXtk0XlVw7ooKMGddFU0UM8eXtCydIFSAEZD5HDsVQQQmTIZHL5ZIxOaWZDmmZP48Xy3IEbpmBM3oA3TOb/Wv8ndP8MAN00Rl8w7SJqaUG8FaEHdWcjQW4CFvWrqPXipvY0ms1y3WdKM4LUldIQ8nlzpBeHNPX/egUAH/RUL2X8Bd73IkgD4R4IkiTkNWINFbomFrBFmGk9rZ4FyyPm2ypJseGwdOxCIVpM6GOm0HDqJ6UpTDdB5XZqIFdqonqDVA7ZuNVEVNgHsvQp+cnhWiQwxp5Ol+O2cPWR2IxhTu+6gapyHhqsvXflgQixd6/conaJVqJ1C4WheqLuSaOW3t0G/ji1vrOhy5Ya0WGuLTzczjLrNcdD1cWA7WvW3QUu26mAm2CzWDmzvtU0YTIYjHQVJE2TEZ11fz+MTquNgtEYMyOzxantG5knyrz4Kkses/J1DusPIlrfhk/5IPxLT7enfFos2LoA8TD2nTyxcHBKvRUObDw42z2nFSI0RR0kjCzVoS4yrx9fssfX3zGh9UlP+krxqq2hVoW0t5zvV3wn7ZvAPD57gQwH5IXc0BsHtSj778yRCB4RKxgTvCoc2VtIrOKbSIKQpWpFwO+yizOE4v3nlCdb+nP4Oa+GepdLLZc1HZfaNxIQui1IqvQunG+7yQcMXuiOAQYC047vustXc9ChvJR7dlKEis3ECQy5sDjcc2o/kCGSSlGhYHWxYM6GsGRCDIWUjZZihZC8In77S1v5wW0XVnAfbvxEo3ZMl+Ro8N8oKU4imSVYuwlBLF/+2fm6ue5nD87nn3Nnb5wybjnmL1NN9LJKMsfs+EF3fzuy9HL4tELzqv1QYVIkiL/2TscRVrlMpWzynDrRhrNhL3gbzxu502eCZAzue9hEFI/MCbMDbyAcDcvIKaJ3XJGo9pFN2bPmBwhP0//8GoOYYrgs8lYKdmqMcN2j7va4rdCpQEJjsqnr1UBzfL9iY2ei1YHtsHcVJmLJwIlei6jmNFGVkfKCmLOCdPNbx6qtiCKCUkJUZPW22PGqU34NomryYQKwHqVmdvYOravB8Y3rRfVjUJ9ZVLx5jrjO73DpkpSXJ+QlNHgSAtvjtrepPkaxF4zHCIPXVLI1js8rj2plplxJYM0ijZKWkFeO+R0hTvz6DCQN1vIo33Y57i7/6CHYDJy78i+SNELAO/PhNQWuXdVzqGYYLtDrjt01ZBPKnLjC1i2/c/knluq+vMprHcfZyBeIgElZqiEFITcWKuAG6zoNK4cqYVxecSEJ1scq7Pd70YxAL4ub1OKAKmCuIBcmxPwN/iM/nbHr3Ag/17eC5CJcp2ZYGemrcNg64WmhgeXsGzhHUX/y4y8PZClIvqGcR+gTjRVpPIZeTIQ/tUG97PBrp2yQJbMXBzT9RJ94x6sF6izfvFxDeF2oLnucZse7Xo0vhzHVfEeFgukrmGRED9AaT6yIneaQdHhsrb91/oB7xLidW4hg6mIKrPKajrvZ0OiMiNQWPJvul0cWPLyK5Mqrijh5tdkbB7yikomemGrNTepZSU97zWPWLmez4dT/iK+xTbW9DkYS+wTvnK49RI5V+i60q744o7Bzf/uXQAWlyOr39+xuNziqkxYJVxtUaBS2WPG1iC5MPjLYPnMCvQ50BPuFDkEZeFHOAK/GTPo2qe6tGsxg2gzjzJGfJKZZ5WSMFIRCsg4BtdViR1zhaGDhBOZmT0vmdbF0rppcvNJgttpzZNk7z4ZUzl0NnAbNHCbGwa1AKjGJWBg4QbOwo7WRTauJfBi7g1aFvZugOZLz3Y8KT8oSThOGe4nBoH6ouOfvv5L/mj1GX9Zv8HVZkEfW3JvVX4XrS/88WbJXy9eA+D+Ysv9hbU+tD4SXKJLlSkvVEh9gNuA64VPLhqW1UhUz5N+ifNKXJr56uRvEFzmp90D/nX4Ef/m9n1+8vl94uPFAReU68e8G3rLfHcjSR1L37N0A5VEYvakbUV9Y+A1LtXyxR/u+T+/9R/5b1Z/xV+fvc7/bfGnfLw958vNittPTwi3gf3g+NfyY/5q9RqffHrBWYZx/QInlKnt5+tuBsHjFkukCmhdkU9aqCty6ywmrnHkab4LwuKsY/n2Fe1Jz+Vyz+9f/pSLRc8QHNtFxRDMTPCk7qh9tIKIBq6zgeQzv+PcK5vc8vl4yja1eEnsXD0br03XROtG7vtN2efCSCCp0Ehk7XpqiXwaz/jb7UM+G84IklgEOy/eqW/5g/qXvF3dkoFkSzoGhY0Kgwq1JBJKp0IbOv7o/DMuV1tuzoWPK8ftt+wRfLk94d80MgbCjxb9ueS72fdTSPtXT46J0Z4ixr4OpLuj5ycGfdpeLj1QM4P3tZ/z+Senfp3xz6s2ysKxqP/xzhYAgVzMiARXl7m+VIrD5HSq2fo2h2eY8Am8Ye0HOVNS/uRQUS/bmwDlxMgevg7V5N+FMYlpJiuWSVqjWZEhIf2AGz0eM6qaXOq/up0Dk33AGs921E/89BFBKNZPK4cfzQz2BE5sdjkC4kqR9k4pBwduZCIfDy879KHP/bev6NBgE91wZoWl0BWpeJfxe53j2iYQKDHjuoTEjAYld1j+8LEBULK8dWPCDbhLVgPpXpFn1p2ioN7MX7R2SFMhbdm77uiu+Dx39x/6kCLRPjKp0WCANbblea/lb8rGUHYD1N4UG8FBLBPIZA99rML4uqFT8UqPJOsUJ/apB33KArWM8tyYsZdPVkDJUtzVMWCegx6wTRnGgsvB3f2HeC38Gg7k38d7zf9mmnA84qxkOPkBELwB8JMV3Ivw7g59L6GdR58IuXNo8SVxLhO6SPPLHf4/F/fn5/wd2iu6PgXfkBpHXJbCV1cY2H5Exngw6/u+hzgkeGOBgxR5RfkRHNqCSmH1mJWeWNUZHN/5Qw8gccLd06GV45v/NwwDmnz1YpIDk3TwZLKLzvCmksV6LPtcsQ4952HHpd8Y8FZlSMa8TrJP50HqChogp7v3thcw+neNCa/vbfE/vqW+7KhcYuUHgkuHuQyLNpqYYS/WV2pmwY4hH7xUpkK3PwLV03bs1uJMap5DmRvtb5odnCXP6x1VoXJWFE9i14Er2w+SCZoRl63wzXRu6nxPqVyidQNVWQc3bjTDNrGi+D7XpbjgZyPV6bOmiQEsAH0q+lQu0bho2eUu3llHf6cxM+GW963Ozfd/HKRWkWUk1InL0y3vLp7wo/oRT9oVdR3pS2sWMBt3Dn3F9bCg8ZHzem9FkaMR3KGvWKPFfYat0IfAddfypF2aL1WRmJPL3JRNNXU9LvjlcMln3SnjpibcOnDGZk/FVT8x4ZJoGMlFXTsReqM6iILvhdQoeQnaZM5XHX/a/pJ/3DQs3S/56OSShR9J2bHJp/gOVDy76wVDXyEbg2+pesETyjfc92WKFK1rdFHDegVthS496UHFuLR0kPFESBVU9zzuD/ZUl4l1nXl9fcVr1TUjvvRvh7nQKFgxqleLd6slsvQdtcQiFXd0OeDFFCgT1tNcWnR0ZOF6TlzPiDMzSPG2nQLCBXg6rvi4O6d2iXU2Q7h7fmBJ5r7vS9KToig7daRs7U1Ty0fUiQnfUFWJuq340q/4tkY9PxgQrl6QOlvesEy9Lscycr3Toz0/zyEX/NdhyL9uHMehuSI5sOfzbJ5VZsNv/R6v2nBkWj/ShIHkAl51vtm5MulMcXKejM9KmCW6fAVIgIGzQQP7bJVXkDvgcloweMmE0td2LHt+1ccxdD0e0+lFVnQc0WFAY0DU446kZvN2jgsS5UdZDz3exnRPvT1HwFyZjaPyzIhMiwXB/nPkCY4fVfnn8987ktjCapfquar+lRKJcABW8x/4igznoKpMCrts0CXkZUZGUyw4ZyZ5cSXFGVNAM6KCjKVXfBSyd+TWz33B48pAmUuWeVq8IMtJYUZ2Y3ENPR5xWZjdpOTg6C9rZBTkJuN21jsH8Dx39x/80MJgT4t8OfSDTyoK67tWe5x6Rkvkl/XufV2hR1DvTOLrxI7PZOQ2Gb85Mfl5ZUz89JqJHZfRZNEalNSovXW0eoBkCFubd0yGJ6Ti2OvGw+JwRhLBw6qFk2SFyX4wA7Df9vh1pIjfw3tNdxerMpn0WEt7mjQ1qJJOG+LrnnwpcOGQWMN1ICaPc0pVR6p+JDzq8f1gDPg2H97rGz/L4WHGmDlDTGjKL60n3IpJHqoAIeOcgWtf5le7FPLcSzxmP5sQdbkiixwAMRy8Q0qRW5GvtU2YlVRHsls5KsJOOWbK0TU6f/DDNicwav+2+WbMnuthgdtnQh2JlUP8xL+b4nFSIIooYjlsUw/WC1dQtZ/sAAj7kVRXDDcLYpUZlzWuymY8WxjxqS5kcZ8yg2wkURWZ33Rcpn04SciRQ4F9dOPMgHrJmNezzoWHQzuY/bFBlNZFKsmIyCxuMFCdCunkiOpm1Vkkk8tn8S6Vz3TYvhy9w5QOlKeyzixzp3yu0nOrkzLV3iuqIw+OcCM0j7/7sZiitXKQ0pIlqFNTPWW75+foSF7phoovhxPW/pJH/ZphMAm4emU4t20NF8pi2bOqBoIkMjL3ZU8qBIDTtqcJiRvfMu6sP0yDsutrPtmccb1vGTY1sveFUbDPOWxq/vLx63zZrfl8c2LzdWssthZHfecT10PLR93lLEt3ojMLXklk4UdkFRnOizIig+wd233DB8N9flH/DV8mi4Bb+JHKJ3QiqjIwOJLzNv+/wKlDg7mXEzzSZvx5xDUJL5Y97yXj64FmucFXHq0r0kmPVoHUOoZzT26FMTj2bSB6R3vasW47Wj8SXDIDQq3LfaueZeHTeRrV0WlFVM9Wa572y3n9eup6TuqRQT2bbAqaIIngSsoVpiYZy5r5zCmORCWZpRMqHOc+8bDe0GmgcZGT0FvPftjSITxKDRkrlCSFAWPCR4XJM72SjEc5dT21KKNrqaQFqm/Yu18/fjAgPAehWQy8v3hElys2qaHPYc4LPw6qn8bXRo/9hsOJ4vXAuI94xhyOfp7nuJy53/DvwKhd4qLacdJsCEH4Mie2I/hkC4TgTY5eO+sXqlOi2SjNU6huLVP67rAK7G1q8bFmkxuyCMHbTT8lR84CoVR0/QiqeFfhspuZgFd5HDMYd7jrabJMEd3vyRuga/AaEHdQhCgHc5YJbJc1j8nJ1TIq1eWykCrvoFPn+bRuOrjmTttNarmX6jIum5TclxucUBZIlUerwOhrrvOScZS5t+0rf5VA9gcG8AcrSX+ehLuqcOcn0DRwvybdd4z3o5mwBY/0RZnfOutp8zr3C/vOW+9pV1jVepKiw3iqpAZjU3vLPzXmt2SMW6vhVyTL1oIoyCCkNrD5/RX7N5XqE8fq6pr6dnrhK6bIUVMCUJQAM3td+sryZHQ32KLMD4Lgrae3CuTKkSrBA9o/B2OUCLRcHkVLD+LRfs5ByE0gJ4dWhmJcMUf0XbTWghMlLhzDmZgSYrRYOd9lmpuE75TxRNi+6RnX5hnQXBkQn1hBFTFp9f1L0DXs9vDoKey6Zz/1b2d8kxTxObE1L+K9VASZ7e2VXKTH0tTIegV1TX67YvOPA/3bIBJgXyO/EGgy4XwknESqn+9p/99XhA86ZJtxX/yKfm595tvpnNOMDiPa98WZ+yUVSLxDmwpd1kibqYMVwCfmUdDS22iMzrUuuYktN3HB07gkisMFA4+qkznXBG7t/j3NPTPo08PPxJmCQ2bQfbfX2w7L827gZcsF7E/ziybbzm6s+fDmks/6E4al44/aT/G1KalidozZzyDcieLKdaqNR8fA5BL/osbF//jEPvWpZ/h4wXi6Jq6E/nWIK5A2w9kIbaatRi4We9pqxDFSBQNQgcIwl3bFUOTc09w87eMJkM/iHISlDDzwW5ZiDO1UlLf+bdvDQaAVezQjKYtuc0AQmY/CRD4NmtnkkZFMVGGPJ2rpoS1y+klaP0HySVUYJ8ZQfQHweT7mU2+sz5kuV7ZG2AUWHznOfvndj0V/UqK7asyctKdI1Aur7IQ8OLLC1jf8zfVDngxLPtmcMewrZHBorfQ/7hGfWawG3r94wmuLG4Yc2MaaTWyI6hiTpbesqoE/PP+CVej5YHuPv86vMTYm7dxet+xuWrTz1I89fl+KT2XtorcVTx/f54m/Z33cbSafRiRk6iYSQiL4zOe7E550KxofOWv2tH7kNPS0xZDvvNrz5sMrniyXdNsa/1lD2ArdYsG/vv49Kkl0aoDufrVhFYZDMTqD2zl0sKQNSfr8y/JbDF025LfvoydLwus91Z9tCA97ln7ksh5ZuEjrO86bJ7Q+kZ0j+YrsTA0gtSJe2VPzVJd0VEhQXJuQYL4EO20gQsSKh1HNOHAqNI7Zsyvg/HZo+Oz2lM3Y8OPFE/73937GjxZP+XQ85X/ev8nNuLD7Y4DaWcFlrzVOlTPJvB0ip25KxQKHQ7Tn6fpzHrbXtG7k3Jt5ohPlWmFT+sW35TM4MsGNOCzVQJ3QuEiQzLvhFi8QqjV/JUug/Vb7/QcDwnFCVUXO/I5KGnbZIhCOM8GnMcmD5l9FeVFdQxbkbjeg4/zwGe38HQHgUPqL/Mgq9GzdaFKoZBX1qS/JjNmskuVV8b0aEz7w3CpdxpjwTpVRLZPViR4lItgqfHIknIzxJsXiKz9mc7mvlhOMSMhoHNEhwOgQTXOs2UxwHkv/jox1TOpmCwE5koMz/0pZIh3JxI8/gyrGqByx6Ye8xPI/5w5MuFZITtbD5uLcw/aVP6oYbv1gD+DzJNzeQdMgywUsA7oQi6FwDnpwCpplBom5grQQclDLLAV8yfycXNNTqwxnkFtFIrhOCoAvvcJ3PCLvnh2SJ3YWcvD0Fx53KmjXsXhRxiy/jTGfz9M3zEDcFmIK2fbTlO8sYuaQFNZMvaBftxiR6dxzsyu5mbfJ/HPE2HINgC+L3KlFIFq8GZosC7Y1995JVu6y5d1W20yurJUgtgeGYoqqm6ew4GDZIieVgRf/Awsr+3UA+PTcdwXi5fe1AAudetGnyaCukUVLPvUMrzm6t4wx4lGN7B3+ZKS9GPB1JPQj4YOe8Be/QUHj6JQzprcAgJyK2eFLjCiTUtyrAgSHn9q8ZG7cwonF5TQu4lKmz4FtaowJR2ajtJkJR2Z2WgvSk+ccs9k2QfS5EvVZuq5fvzvm32cqtto8MybP9dDiteZBWBBLryccWNbDZ5iY8GK26OX51/R3GM2ne3vvTUvUNWndMJw5br1j6AWWCcIAalLVZT1SBesDn1zqK4msfD9LjCuJeJl6yK2oIEUdaB4sxUSvsGdvh2tOXI8ydf2bkDWU1waERgx8e1yJY3JY+fGojaOMThPXeaDXRK+O61zTlx7zTi06yxSeoSg8j1ou1Zfnp0LA9JnD7C+TxLblVdHBE26hefrdj0VZ4hsTrhzdM+0clOLnoOKIo+emM5Bz2zXo4O11TWZ1tud00bGuBu41W9a+ZwPcqjnVp+zYx4qkwiKMvNbc8LC+Iarnw8UFKTozguu9ycT3jrCxYuscGVoWQO4GO6/X0L+e8U3Ch8Rq0VOHRMqOXV9bL3Kw9dAQAl50zqFeuoGHy1uCy3yWT8ja4HvzW/p8d8rPFw+oJM195LWPzC66agVgTeZNIy/SkDWUqMbLE3gj4P9oILxrBcF1o6x9ZuVH7ocNKzeYbxOeOZIPW6tuc0MznrDLzZHruCNInpnwqI6+gPC5NRLzitrlmjF7rsYlH+0veNotOXUDJ9LzdnU9R5CZl4Sx514dEV8UG75ca5n7/nhNKoUJ3yLeiiKXbkvrBva55mle0aWKXgO3acGggVoiK6wfHISRAa9CJZlTP7CQzBeuovoOkoSXC8J/RVV5qtymZ+68vjTfP29Mvd3wVWYc7vaY27ZMan4MsD3ZGKgjadRXhjDHLh19YOvhdT8AOeGLGkcgbzLtqCUSfIJKyY2iVUb8MQAvfeM5EfpE2CV8l58LwpM6uuTxEYZscsI6RGLypOSZYoDvunIfpNR/l4ZIMW8rfWNwBKblGMbrc93ODzx7+R2lgI8iT9M7h9sWbTr141s/2h2poK0EUe+I4rkdG/adsPAjdZNw7u4CcAL5P1gGfBrPkXBrJYxnAT2tzfgkOdzOzY6m9iJmd3PxmEO6t/3vIoS+TODhcKSm/S5Z8COWAV7ZgkSt6jED7gmEzsrQBKQCWDxkr2jjTN68jiZrHn4g8uZfd8ystxyA8TEwcuBTJmys977aWDyeva4A5XTUQ3/HYbs8VU7wY/Oc6bWzumka82KwAApvUkXE4wbB7036P9/bsuLGhBsSbvDI6C2erkTUWY754Q1UxMz6ai150D/Y0tRhTJTps8+9qG0/RwafFkJ+w5EvPP3rnhQ8OngryDTG+Lo44n/eEbTH/6xHtt++FJ9LsSx7jA3P+cj74iWMo2KlihytUYRKEo0bacSYtECCJOw2LVf7FbtUo9kUZVIYaRuHC2nC1jqfbzoXwI2BzsUXR2dfkOncnIG8mJUXTk27IFqywQ/FM6bXC3bvUkEHT46C1I4qK60obZFW1y7OzCzYPdQKa9Zu8sJJj+kzpoxse3zO+Ai1gFsCbYJHEZpMVY/oaiRWmb52bJZLxqohhMy2iXhvaz8/Gbk5ITohT+kx7pBFPBmlrtzAU79i6caZbVbbVfhcHlWo1QC3VyFkZ+BbBZ/tkak1UGHMiW0cGXNiVMcueQYVcj70rmc11jurK/c8W//OQL3Igqd+9i4GNn1NnzwRJeOpRNl8msm/3OMefffC72TcK86KopMXSK7UjDUB1znolZwqnoYl+6EyN3KAYDf1GD37wdy0P5YznoYFUT1DMoBosn2Zwfgv9pdcxSWf7M5I2e7BZEF6h++lpF2UJAs4zB1i9wfAPEocc3V1LO8l5VoKAsEnotp7Omn5yF/yJKy4jS1DAZAhZHbLjCRHXmSG7Pm8P6XPnpthwZA9n96cItEVs1HItc4O5JLAD9/xHlWusbBMtG/ukDeVxet7zk+uaUtf/Zk3SXkjI4K15CXcDLBFFS8WCTaqNxN1SbMaY1LiRHWIhvn3M4IvOMNLptbISixKzu3hl7f3ubnxbOOCv109JCbPjoZLibTtNSIR7wak3CN9KUJucsXP+5rPxY77kDwxO24RPlUzUVu4AfHKQg14b1JLr8Fid8tnK4d9WskaOYtjVGGTlVGUnVZ3CdvfcLxEd3T9ldVzVQtcz0d/kBOlcRaWPgHq5/3BExh/NsLsm8a0vcolnOrcQzKfXEeN9iqFqXIYG/a7PNQWj0EzSzdw4juuqx5WkTgmwjLjKsvQNNOO0RzS48jiaqT7ckCuIm74KhgYs+c6NuwHT58ClU+cND3dWDEMAc3yDIiTGdjZ+MEvWV/YcKJ4n3BhLE6oB1MXOCymnud2Phv1lKHH67KyIpszvVVKP5kBvowwZqtQVs5uoL5U6TU4tLIb4dXWEa8Sl+2O09BRhXTHlfIrBoc/1EP3HAl3XHr27ywYX1+RFqCjo3okdwyKzG25pFM4wCtaK+yEag/1jRIXQqoN1B/ezxYgYQe+MyfRtLBJXXLxU4gym5PhbcHlSq54Dsaqa1DSaYAH58i4hH2HPn4K+55XZjjIwVk/dokam8DytNBwQ2b52UjzNOF2A36fZqbajRk/KBJtutTSyznfLhQOsXpyB4gf2HaOKlaKjNliGDNo7UnBDMOqnSc/PbDyYIt5tx/xtyO+qQi7QFp4fFeM+orxHirFXE+IC4+LDhk8fmLef+jjRYHR560BniODj+eOzT+tGN6rScEzNBW6ceAVTkbEK/4XO5p/dUX90R7ZJtznv1lMjEwFAGctInEJUluug8b43PvC9zV08jkI5iMRsVQQHOZO7XazHL2ShPbCF5+d8+HVQ7TO6DrR1PFQBD2gbZ6tXgvMkZfOKcEnvNMC1DBPhFmBZkA6irUqZTex6g5xiveTBP7u+4or75mEtKnICcQFVlk5d5EzH1mHgXVVGLVcMnqDJ9ZCbjxU3nqyX+ieLvfufsR/eQPO2g39TwbrqvZqhlwOQjFi7D2Mi4ru8hS3qMitkEr/a64hra2ImoMSl8WcUbBtOb2jEvDFqXxyM59bBqJFu7okSBbCCE6lFHOlpDGYwtClA/iSpJAy2kVIiiYljfaoWdExm0qIw3pXzXHX5N4ccpGn9YQAaciM24E8GkB05XHsMuPTR1Td1Xc+EqG3zxWLr0euDYDHpfVYu85RX5mBWa5huF2wb1oritYZbS0RYBwCcfRsRHnMGkSpqsTJoqf2ZU2SLBru6W7Bo83KVAjJMY7eztUkVDdCtZV5XkgNd7w9NEBuSlpHTfFuyKjCMAQGoKoSZ8u9Oa1nRxcDKTtuhoYvdidz//J0fS2bgeFBx3jmqerEpm/42+sHPNqs2H2yJmysaOK8kmqb87XJEJS0ryx28fY76oALnmkuRy7/F49o/9hzutzy9oNHnC52SCnWzecw2e5P6tmmpihaD4aGVqozt/JRPVG8+RXh6LSmTwfVpmJpAEs30MjIwo1cuj2tjPzk6g0++ORtnn7S8OjiHv/38R/Snve8v7rmv374S95bfs5WlS+yslU7c1MByZ+PC/5yuGCXWnZDzePtiv1QUdUjq+WWqhpZ+YGbesHSDcbQTz3qx3+L3G0tSeoY8USEXaoQhC9TS/9KgPBfNYqs4lkWHJhd8DzmtHn8/PT4bN73NI6N254ddxh0MRhiH+XAlM8FAdEfPqP3goaoFnJQqThmwrNV4SqrgE+9wAcmPBsTvk3QZZ6XZJFV6FIgJqseOadm/Z8M7B1Ts8/W8/+OEeHAYf8ek4R6hDC+ye38q1s76gc8mnIn056DkZtatNgzpjnMLIXJkDajo++t18eiBK0AgE7bPbzPqzZyEIbTwHCvsus+gdtReogpenTmrOg8tbV6AMuW9oNN3DItiI+OzQzge8EVR20thkiSBBeFLGrHxTY5y/PEa3FKP2bCiynID03e/CtHMVcr7uiAAeFCw6mAJKXaJuqrZMkB0ZxL530y9ZNP27NV77QxQIskXJ99a4pA5PCUUpI6ynXinTVrupI13cnBbG36rKN9Ljd6ZDSA7iaFRGGrZnKyMOG5UlxwL57p+yGPYzf0Z4/FM/9OLfSvO7ofGwOuG2cS1CYhTUaahEsj/sOO8Ff7b/+ZJoYrMHs6zLL4lz2KUaCKzP4eClSYBD2QjAmXhEZht224vloRViPtakfwydYuuSyFtTwexY/Nc0X5Mhbc2DuyGU6S7TW+sNxZTUFo6vzSP47OTLgBESGloxvcBMKjoINDBweDo87QitIUJtwIkGIdlo1ln9RW4qeb5gsc0+ZSxvUjaEZiRLrO4g/vHA+HVhXRe1g5+ocLWC4Yl57uQU1ceDtPz80YLNdKPMmmajJK+7AfjjmE4/bK0p52yLN3dzwwXAS/sznGRSV05f4STRnkorXMhH0qRb9s98iUIWWkH+37o6HOmUGkO7oenx3DALdAP6JRLYkgTguz/bf0gb47ZvVSngquhQGvitFZr/heqG/Voh8x49LYKrHOSCg+TrFApiyQBLIQW+vP9m0mZUfKpgwYk6fb16bMcNbDLADZwL7f25wdF/Y4seBS2sYm/5YcJhUIxTPBmrZ98UlqfAQC26FmjH5W2aoKlU+smoEmRGqfWC17xsbA3xA9MVlvevu5p3lqxcHuEvLCQDiV4qqE+gqXFN9/t3vV1N4QFpnl63tW7ynnfsvD5ppzv5tJyYy7I0FP6hg1mMHy0SltRs1pbtGQcq+YmPDnDVP7RFau53645cT1PM2ntLeKe+LZ6YLH5xWjZFoRLtwH/IP2mqfFvC9kGDSwyw2jOjY58MGw5vG45rZr+PTmjE3fcNp2vOM9J9IxaEXlk4HqkoQQ1c+FsskNfWbC5dCmfJwwsNPwXNz6644fDggHcrb+gQloP+tYPo0JfE/M9/+/vT97kiRJ0jyxH4uIHmbuHldGZlVXVU91T2OmZ3YWhCXCtYQH4B3/MAgEIhCB5mEBAvYcYKd7uqumuiqr8ogIDz/sUlURYTywiJq6h2fkFZkVkW1M5GEe5mZqanqICPP38fd9FTIOd5P2r3oNAOq+sq88uGw3YLDF+T+rhVONQi+TOrE8AHJKVpgSMpmX9EPKskkdh9ggY2uCez7RS4QMXRNJ6mhCMsuzKva1GMA+JM2p+5FViiqkO1ICOVbdjhU4i2phEu61O+i81hFUK13562Xrln+tRBupn79YGBg90D5jyUqZL3sRNEOehDwYOi5ZS9/b8ZvBIvn8wG4Z9ZBXmXRmxSQpk3tNqOZCSKmaz/3Lc0/A4gwvk73lcSiFriNgJcciVLa/uZChyzgcDNbXrGCU9QjgyK0jrTANAfeBIKuLkGoNVhAeVxJvckHlKpp9VDji7d+yJPDFWqZUro6oeKGbVwqklbgzEpMl3Y3M/eN1c4ohT7rXI9IuYsXjKcIwopOA5KPNWr0u1Aou3tk2XNRF4eA9H9BEim2Y9WYSimq1swKFOqwIsRts3H9bfA0bLq088VlHWgXGX/WklfEvnVNcH5Em4YaJ8E8H3DjR/OcD7qso6F81R9/7bC3XiRYv+lwon3+WqMWaBw/PEc3MZRFY2RXHIafMldkS8bmNC2YGj1IX3QX7dKZPkaX0/2ZXeppBNc/K6g+dMlXISdDk7fbKx1WadxnxQAQZHeyBURiSY5cdQ5mBvGRU66K+UFS9mEWgl3e/1BrH+4fUCmxtZwj4MpxDQhGH8x5Jio4RJ4lwlZCd4DujMadW0KDE3mwpKzNqTrjnxnveTMLB2p1Ku0VVCidzdNEouhhmm1iLuDoX+lSK9WUVycoONKO9HAtKtTjpxNDPOkct5ud53BsxlfipQWNChwZSmq04v8qq99vEsTBZP9tYBHqWCH0k+oZhElJfChyrkgD3GX8eabuJOHmmXQOTK+JuJnaasuCfZM6bkSEFxtiZEODkSXsPU1HedzoX1McnmXhuc0JurdAtkxAP1n+dGyWtrECgXmFyjKmFaNeATML+IrDtR9bNiJfMRTeQW2E7tlzdrImj5SPXYurnLmRWq5E2RILP9CHiXWaMnsOzhhwcqVfis4hbxdKhYWvF1Cg5yOy3/l1j+NUjAM4/OfBofcWzMNDJxC63c25VW8Xm9YqYWvjKjbR6l4Fk61bTS8jquZCIqmPCscueqaiYmzCbKRJUpf9Rgwk2q4NO+cufv8S7zI1v+ePhnNsXDYdDzz+65/hzz63AH3Hciom97TENhBvtuckr9trgUD5Z3fJRu+GsHfm4u+WsGUuLTzQdhHIPVFp8TcLb8ihg62E55om1dcM5QfoO1qvvdPzfmyRcSjVpl7ojTbwoos8qjSVpXibktSpxp8e7CGjUBDyp8TyzOnwpvzUu3qW9c+T7V5n7igIGyWZA3wupK1Sef24higRFmoyE5eRyrBRJTrjDiNsc0MOIPtCbOibPfr8mblZctAd+cX7NRTvQSceYPD5k1sGqhPWGyAopO1z+po0G72dEdWxjyzgFgsus3ERwC4u3Qgyr4VA6H2m9knHEukCCheiaJcwihXiwnN+15nV3FzN1IHVSrSGYz2elpmd1VuHN9ugWgy9iC66088QbIYvHZ6VxyarRi/2r1K4PjUWirRKfZMaPI25whCtvvWLlWNcF86yyXdBpvBYEZxE1aXOL06OLx7qQVks6XUHXtVNkPeHXyeyLhpacbYEnSQrI4pjOGyQqThua8IEl4dWeLGacCoyY6ExXhGdqf3WhXUq92Gu/6ENfVkxJXr0w168qfb1QNiVLocJjbRxTwh0iufWkzgobd4pHIjRbCIdsVnNrW/QSI7rdoTc79FEmy5nRKpPtg/OGSIYdR9HKQ6HQT4p8PxDj3cf9JNl73KpHQkDbBj3roQnk1hHPTJ3eXe0Iv3+BXO++fvtvqaLGZx03//tPGH65Rh854hObg5uQOFtNND4jvx0I//4G97sBt034F9ObGyrU7vsCVneE3ygJR6HCpg7iSvFvCCT+OCFFX8aKRDrvoxS2U11xxLreyc70IYr2gEfxLlsCPhnid9w4VuSaC4dmuVV7vSex186JtFKo5kfEfO79Xs4v2ZHGQkGvIClCaBLdasKHDENAtx6uQc4ct2PgZW65zUZhbV1B79VaoBKe2Hhyby0q77qomDdb+8V7pGkQ75DQmABnbTNcMjbq6twJjAmJRX3/Mhl9PThyH0xMTkorA7UqckzA5Y4w6eIgzqKgDtxRDf7oeyKILPplZLGPchwDNViRbDnfqtO5qDTPP3XIrO0xthCw01d6stWBxB53WJswZRmfyYofM81twj/QavitY3ktldajtM48fX7Lzy9u2Ywdr56tzRPbmUCYc0rXTDxZHViFiT/dPOL1647mxuNGaDam2bH/ueB/pfzi7JpXwxnX+55p8kz7Bv+6Ieysdam2mMbzTPPrLU/Oj2OYqjBGz3bfEZPDucy6izQ+sdn2xJc9fusIe6F/aQKdu583XF/0PFntOQsjf3l2xVkY+I9XP+fFp09oXgf8QWhurBXt8DEM/zbz/KMtZ83Ix/2GMz/y8eqc37QTu0PHRTfy84tbnrR7NrHjxe6Mw9hw1XfE3pP675eP3P7XvwTg+V+/4JfPP+XX/QtuU8+fpqdsUm90cT/eESVsSQSZWPvhDeFsT01elRa4EGgEbnLDH+IZN7nhNnXcxp5N6lAP5/5ggGq2/CtIwl0o//V/9R9xo/C7Lz7m//E//jv+6cvnXDVP+L/8w7/h3zcTQwu3Z8LQmBZDDGIFxV6ZnpiG1bNmy797+gVPmx3BJVZhmjXGdDFHrMphDKSigG6uB0GqfOIx36zPO5SmdfjHF7jnT7/T8X9vknDKvPMQcl2TaNRM75expJpbkz+ziJWTDIUyULe6pA24eysgV0Vh7mUMTooXYKjIyQe1zH03ITYx4480nOOfdEbCJSZkihBTEfm6Gyk7DlNgGNvS659Z+5EYHF2IjBpognn/Vb/4Wt2f0dUPNFSFMXtyDmi50evqZS5kCPMNLwUJb5yQVEl1wtbj9qQohmjNuO8dn/t08CXyukTfj69nVkV9U6hQj/+qoJNDBweTM0EZyvlavu4DPV3qrOKezwq/oFDFVVlQkbHv5+79iB7HCHnwtBzfv/ypz2VKsq9GuesSmoRcxGBUyj4UtCu3Yj3lrbuL4H4IUb6vFDp/Hd6PdO6iNH4vOQG++tq6c05qBsKx4FGOb12AQknwp4yYL5ChC/fOnR9tcR0VpBObPRdIOFOLkktBpi6G7X51E2gEN5po34yEv09VxQfo4uLE0MC2RboWPVtD10Dn0ceN9e4qViT6npFWgeGXaw5/88hQyT4ianTppo90TUTSQPjDDvc/v133QESOCUt9jnw83MdBcG4xqS4Gf64xy1Dwe9c4dT6w56rUVy2SzgW82sOtxihMqSRzFSZfJOEiNnRIKdAuhU9rEVUyBksUurqX9KA2nyaHpsUfFPAYLbdJqHPoBLoHBmFInp16BtysYeLQuQUwI1ZAKwjfuy7e6jTNOy+hLH+9s+s7BGql+f7nSi4FnFTWOMOITwm8Q5rG2oByNlu7+yhxTcKXnm/l+fnHObTJti/LIqMT1Bt1XJ2Y7ohgLJSmMFKcoE1t66G4NNgYFjt7nFk5tYA8I+jH583Jw14rSfFDc3R3yOUYHDLBR2s3fJdRC/VBebre85dnr9l2HetmNOak6JzsdT7ypAiGXR1WXCVrE3KDWeP6A4yPbW10EQ5so8mw5+TQyeEPpsdSjxfOKN8fP9rwb59+TlbHkD0xe3ax5apfcYgGnJw1I95lPs2O7bQi7IRmA6uXmfY2kfrAfgxkFZxknrVbnoUtvw3PkdERbq2Yu/4i024yuMBtdqzCxHkz8Lzd8Djs6dxEVuF27LloD/x6fcnTZseX4wVDNOV6mkwOgdx8v5tk/OWF/fLxNRerged+Q1LHPrW8imd0LqIidG5CgbWOUFpRVzK+kZN5jkjyWpTnLrNyyqvUcaWeCVM2zyqMOTBJIBWBtwqoCsqTds8vf3bJE7eHCf6HYaR76Rhcx29e9agzYHR4au0gdv3a3OvPJ7p+R/ARaZSP+1t+0V/dcSo42qS54mZgaz1z+zErwvth/CGz1TTrs2yMnb5FVh84Eg5QR76vVCgv8VUU9GU4yaRKa/8GK537CXku1KiEoY936IX3d/td2gS8RzEnaSUZtv6vov5Y+rm85NmDMmXQKZnP6hQfPi4KOTry5G3AL9XzmZquE72PrPzIyk+Mzpee6GMX8wcXi4l2uZKpCbEiDBrY53YWBrS/V7Taeq0fyuV03lZdYJXBZFl0f8vtZIqo5aV63NJsGccxkZ/dAcoqzo1WzfUTpjIr1U6wDlEVDV8kNO9zeAdlQSVdj+SAjAKxfP+5Z5l5ccLxqTto0ExzfeCEVYr1nT9V5KQeX5iLT+QiihRKr9xiG4b62uJJapHwQwt54AdmS7ZqFUYsC1yR0tNoGdOdY7m87ufcRY7ia/c+4439QObrFcpn30nY7TRV+nK1NcM7RIVmB/lGcaOxGWSxHyIy93BKzIbuv0/9NQ/QxdU78rqFVW9e6heB3AW0kXnBjhck+KJHUJKVb/q1nIMmIM7RnAXOzieaix0SMn4VkaD4faT57QE5JNxvBth+gwRA4SioYE881B6FYglAo2inaJuPfbw/YmhNvLyQnZv7L63v0jNpwEkmKAVxlUJdNkeLahnqfaZtUjmN1S/cvuNsdeYyjTc3i5yLaFVp+fOhUjP1qHI+J/J2X3if8c4KguKSJf4LKntok4m2+kR2pq6t2GLz3METBxsB1MTnJjXl4pgdqlZIzE5YAsDv/oCrJcy1QCcC4Uir1TcqDovfc62SctzB6rDh/bFdbws16UMAAE1tSURBVJFk650kvPxzJwkXNIQFw6e8trI1yvPz/LvYJ1E1q8tsCbpEE6LIXpBRjj7XVXejzB91bVDnqNpyaUl4pb4Xynuytiy/j7jrAxy+vylwVfWeGU8KROF26HhxOOeQGg6xYSrte403Zuw+NuymlqzCl1fnNFeO9tr2mTI2A2wPLb/fPuPl/ozbbU/aBaON693CQz2Fhxh4PRaaeJncnZh3eij3TP09uIw2ahT1zphRot7aEkbH692KKXl6H7lte26G3ubqFlK05NElZ8W/yXEz9GQVLoIVF4fcmCh1iLQuzfuT1RXfc2d2apPivqc6en1/mjw3ccXLdM4m9zgpbE0xi76onkHhmhUhdTQucuYaGoml7m25QkNCndCQQOESocnwKvW8GM+5TD3bbDZlVUA4Fip6vR3BQJ3Xbk1SR+yEv/jFK0SUPQ1XrDhIwHeJ5tGI6zLeZ0JhS7hVxD8acH3kWbfj3A+zuLHV/WW2862tPXkBUGWNx/uirq0p9w3GJKrPqRdSH4hVl+dbxnuVhM++hN9A9uFtibjHEpFckLlvM1wsCwC5TICACSE1pSd8qY6u2SruX2O/9qGGK8fSu0zwidAk2ibRh1hoHZlUKkpjFNJuQDfZ1GXTm0dekyMdAtM2kFyw6iTmC34eRhqXOfMDz5odaz9ChsY94q71ygcUUpAkEfABcDMiURPspI7bvGLCc1O8E4G5Kte6jGTPJMWD9M7KRNDjbGzVuzK/5gXC8RB5Q6RYoKF37FJqf/lSTCSXkoE6QygkC2FrqEW7gjabgJ+p6NqgmnDkQvf8IJLDpkGePkZWPe5Ri6QO2XhkKt+95BgzgaHEPGSozGKEOchsT7YczkTLULFAsaAybXRO+IHSc+nIU0nsO0V9xkVB9qaYq1gVGID+AznOy1hcl1awOR5PN4n5pw6KDJOhzSUBVwnQBNvAAlGu4oLHPnLKItYVROi4KD1+KIvFMkf7l6j4yZJlFZnfj0JqlbgWwkrQxiMhEKJn9VJpqkofi30BUMUPihsS/pDQ8QHU7M8d93um20B6fo4+Pid1jvFxIPVWcJDSs+8aB12L63s0JXQcv/l82ATcowvoOrrn0P/FHn6xx/tMFya8z0z/KbH/v4/Ef0ywzcg3UEE3YTJsUafzL8fvt7x/vZLPMulRJq+NyfCjhxiCSePJwRR4RQNDtuLsNhs9spcJj9E2Z+GuTGFMJXxQ3EqN1pkc+7EpVFotFmZK5xPnnSlHD9FzM/SM0ZxK1u1oyXNFpsvjGE1d2kmmbUsLldNC21Sm5NhNLVPyNCGy6qzPdZpp2tAL/NzBrwMcokNyYJ9aDjGUvl0P6u0eawQpt/cPEjmh41AKboIc9ovE+80PVW/94eL8IsEuLSu1zcHZ+cO5UlBxJoJmE62NL3fOeSm8lO3NifpiXoDF3+fPXexitSqL5X5L2TQacl5oXkhJOB8oCsw6G7ZIqAwga9+xBJ+l2NthgMubd+LA0d7YfZxDwE3W7uUOjsvrM4bpmJo4sWvXieJ84ubQ8fryHN0Hmteei9/B6jIRe2F8JMSVFWZ3l2v+Y/wZ075BLhuaWVTT5makKNk7QIXbXc8f3BM6n3jUmSUXGPIeSjLee2sh7JqI9pm0tvn98FyIZ2IOCzvP65cX3HSJ633Puhu53q5Qr8SzjLqj4nvqQfeBL6/Oue07KzJ0lsy1LtG6xMpPOFGSOoYc7H6ZGkPW90qz/XauEPejKR0a467lj4dnTAdb73mXeeq2c+I/aGCnLS+mc5I6OolchAOti2Yz5qy/uncTT9jRSSTj+DxZb/nltOYf9s+5jOty6VeACQ7aoMls9MYciOq4debVfeYGwiPlf/2//XvCCF/GC/7D4Re8SBc8anb85dkrHjV7Vi7yLAz0LpGdEptM9iYynJ2QpRQxMCeG6ike1c3rXcUQ/w5Dwo/P19uwFGfKnSwotI7pacfwyfo7Hf/3KgkHvlEC/k3jPrp9P7K6B1+T6+TDotdc7i4Q/zlEXUZWRLyi4HeQcJfnXvqUxZLvsaIhby5m5sQienJ0c++wFCQcYUbC135k47qjQBs/3Jz8g0Wd5J0r4kZ3i+qWQhsSTjbv9KMiOcXHtYpAHN8zHwetzzCzFsoSdE6ev0qu7bidkmQshHxmBBHeXDhYMbAkSVaN9Vlne8D62RURfiv6+D6F98iqh7M1dKEg4Q6r4smMEKBGl15S+ebKh5bKaUEe9H4f+AM/M6BipdYFuGJIeK6082B/17JokkIdzcGSfx/uCYp9SPHGdSJvIOESjf6pobGFrnd2Oc6J9GJb9bm5QMVxMbrsJb97M955jaBF/d58WY9235X6Vo69N7Eyl4WwB3ejZA+pxRbfaotZ0eV3MfXi9ywFfyM0OPKqJZ93pF6IF4ZKuGQ97i5SFOQdUpDwN1DEt4Q4B12HrHr8OtKeDfgLW/Su/ERwiUPMpN9N5P/wXQrdiwT8K/6EKNpktBNjmvw5Clkz9bgi4XIPCS/6NqUPUedKq11bNSkWybQCWTMinjEGUlE7d84Q7CYk+tYYZ+IadtGEkUJI9E2kDeZ2MSZvQERSJjHerpSEqKpAr5uRUISkJufQaPN4462lTF0VV7XF5pmDxw7WAqhjyrbAj8XPGi3U7dJK8oPdHwosNGu+9nO8R7oi1uVKQX1mRi3e7V1Bw83KsyLbs+/5MuZkuGxmMbdyv/WmvH4ev+pTZeeNVWPJuDvEY1K+aDGZk/CiPj8n4bWIKRyR+MXzkpIVQVNG9wN6vUMPh687Yl8b1cLWT1raj6yoFA+BfdEh8N6QTShjLTDGALcNza2jey30V4nu9YS7CIyPvCXYgOwdo7TIwRO2Rlmf0W+HKdgv2K3T5NkOLamJrJuRXM6XE4UFAu5EaVyGkK1wlsWSaWdzghuFvPOkSdhKxzgFptFaHXKrSLLXo0L2IEmIQ+AgsJtaS759pPcTTSmuwRG9TdmZm1Apwsn37M/3MxLuuI0rfJpoJJY+8GjjT+rMez0HbmPHmAOdiyRxdGrU814n08/C0cuEOJ2F1kYNXKY1L6dzLqc1nU+WwBcqu/nUWzvyPjWzJlfrIoMGnrVb/uoXlzz1O1bjU/64OWM3Cc/CLX+5fsHzsOHcTfzc7zmTiQgcVIgq7LTly3TOLrfz+mlpzVePa1QjpHtN1u4zD7G2lpVS2LVbVOb6mDrIfSCdfbd0+r1Lwn2BklLxOvbkuyrm3yCc5Dt09Ie8w6soW03E/QPDcC5onm2UBSX9A13ofsuwC3BByS/PV9XutiwIYnYcaBizWO6d81cm4TZRgEwC0WwjUlFGnJFXOPZblOuhepC+74vWt8XRY7oM7mipcArb2LGnYZNktnGQwuRw1DS7bGcujdz/gJqRyNGvdXEO6juOwFxJ9ll2HTK/RheflbUIXhTlWkTwg4IzqxSfTViubumh7b334YTceug9ufXzfX43SeZYkCv9ZPPvNfRYsLtTtBPM7iqoJWcwo6RvfEbZjoyOoptk2hRBIbm7+1JphI0gTUDaFtVC336f6M4PhS4eldmiaT52AqoZnSZ0GNC2gVWLdgFt/IMJX0XBgZmZsKRwStZCKzv+TYP5lBvSLfOi1oT3ZC6m1XNjCTUIDtoGXXUQAm5I4EZoHaltbOGrQDZasPMyW5QR3PtfmJoScrMzAeEWws4hbVVqLkWKmwOyH9EUTYzz21xza+BfZHiWaX898ezRhr4dSXvP4Yuew6Fj/ONE2ieOJ/Nrovp/L35/W3iXWXeJsFK6zno+f+yoU4M6wRXErfcDaz9y5kbWbpgFZz22vsHpHZZR/ZpOsrU+lJ74WRldZc636vwaXKIrNOzzpigHh4FdanmVz9llb3P05K3P3AlRzDscD5OzwSljFpWNL8rotdicrYjlR8iTsknwOgnX0XMztNzu+yJ0ZHRj9Z7sIDuWU+WfP1TRlBEimh1z68b9vu6cSl+3m724dYGE39kkHBPzWgCsf1g4QcxRjofU4nbdpqr1qpfCgo6RUnlZvHeBfgtUh4M6HtbtLwsB9XlN2XQvssIw2PruHcR0bunHcOE5PFOmZxltM6GPhCYVUMFeW4tCSgEkHo9MvSc3Ab/3xJUQO0PB1RfBzVZxfSILTEnIxffbF991dZA6Y7hqn2jbSBesgJSyIbJZpbRJSAFElNZFuhDxXSJNtkLNwZvLQKOkdYbeLNSsiGDHS5KJjaImBKkO4rnCWaQ/G+maOINaFYi5L3oWXOKsHUnZ8XLdM14Exift9zoPvvi1u1HnITaq5zb2CHoHPc5A7yZDv8s4MGVPxDGJx6EcJJAzdC4WBN3YnfvUoFD0no4/oYxrjcSiEWHs0N5NdM4KAqDstSHkjoTjadiiwIXf00giI4zqucqd5SLq2ObAqMJA4Cb3jBoY1bNLHWPpQR9KT7hi6+6sQisde9fQSmEYlIswuCrYlhklmJOTJG6l57ByjOffbbB6r5LwpfjCVxuGWXwVFX2mT2MVmaWH+Nch48tkv3rj3aGjt7bY/ecizFYvzElNtRRhFgtb+Ymz0r+yzy37DIcpM03R/CS/YvEjWXCDw+8F6TxT9AzJ6CdTLtQQFZoijtAUzm1SwX+QVISyICyTap1fpSyCGkkMOfB6PGPIgXGIDHmgSgg6yQQRYl1QlTT3TdG0u/nb/Nw8x9f+87INtWez1nfqvctaSjHc0JioniiO1DhzCEBoN4ruEt2jTBttEDUlybuf8V4tpt4SuRHSRYM+7YkXhXZfLmEVnRdMlWlbacvzI3In6VtSAe311nOqvcJYVG9z7e22gkat0Fd7Ldl63ODQVpHziLbZhMW8M1uagrjWaqycr3EXoFMk73YQvx9V7ccIyaYMLVUMbfZjLYspTeTdjnxzi16ckZ+fw6POkvXwkEgHi17GZZWDha/4sadevRVf0up4zl0sC9DgjJ5c6aB1n6NYz6R49GJNji2SlXA7wvWB6XHLeOGInbftFcu1lMWst5xDRv/DWsrdF4L6pu9ZFu52A+4PL4w2XkA+nL2mMhBkyrCfyFMp+nyb1qznCv+HBP86cv54x9/86kuen9/y8tUT/v6//TWv//CY/Pme+HoCvsW1nO8lIQ991TJHdSHSX+yQZxNcDGj4M9wz5TpULzQh86Td86i75anf8Tzc8sSbanM9M8El60ktSUR2zGjN7O+djIaj2RbPKRuGrlocZKpCcDcQm4nn7Ya/Pf+cj5oNnw+P2Y4t16lnmgLTIRDHQAqmwC4+0wY3F5EB2pDoSHOJWFUgmQiWbDJxp3wxwkWEfzoE/nhzxmc3j+nayMXZgVUzMTXC3udZTOy9mTdqIbC2C83juhwLqMgR+b73mge/x/GN8yYrIP3WqvViF+aM+VhZt97wh95/f1/Kvf7GS+8/X7ZprVQZ4gOOBN8h9s8t/dj+QtD/xZZ/9ckrxuwZkzedgqJXkLMjZcdusGRz1U787a++4LwZ+OPmMX96+ozbm4Cb7FpzI6Re8Y9GHl/siMkzPgqkJEybFvnMqOmphXSR8OeRpo08Pttz3o4FFXXEqQi6levbu0zrIudh5HG75/HFjl3TMWxb0s7NCHd4MnJxvgfqPQeIIqOh8ephfFxsj88jH398wydnm/mzxuRn0HApXDhlz8pP/MX6hqfdjv3YsP2Lx6j7fmlce23jXbPNVkhQYcgNN1PPkINZdvli3eViEY6LjOrZpo5dbg2ISw1J3VHYTPKdy21SXwTe4ky1b12kc9Gszlw00MkfRc9aVy3ElMu05iZ3CPDX3Qv+ZfclYDTzpMJGW67Tmoywzy2X05p9bmaGqAC73PBqPGefGrI6xrwQhCuaFsyIdym6+ISTzFkYed5uWYex7J+BkJ+5x1w/adkNP4EkHHlz5HgIBf86UbZvGtVn/D4KXhMcQ2eLJ6doWRzzzyoJv+NLisy92ZWOnoqSZFLHmBM5R3hbpbQiSFEgvQUJl0zAqv6Wx8qDxeH3Ph7Y4fpUraxmFbapZRs7chyJOlE40IaCi86DsiXxFW++fx3OU/idkHuvrGJ4cx+42nl1ekzEdX4ns4DF3BMebLXgB1vIhiEXOrreY5x8WMwFLUh4XnlyK3fpgxWVrXWgO8wY7iLhi8LDm0i4odnVBOIOlbq+vv5kgckq6DgT25Figza/pnx2Dth5KUg4gLgPx9JPFCtULb7/HSQ8TugwouvOEuP2a6auRZIIHH1OlVltfT7whS46ixIpVkyZz/W9e0oWVHmcWXetPDJMuN2ADLH06eusIzBbC1bl51Tp9D/CXHIvsX7r6+69XmJCbvfzS965e9dK4S8z/KtM2008vdjySXPDFHv0s8DhN2u4zrB33z4fW6pQf+VrbHHdtyO+H0jdxOC+nY7MO4l6rTtwXun9xFlpyVoXJDzjZosy/wASDsw6HwZoFBZZGettDlVQmVlYwWW6EAnqOG8GPm43/Ky9YUyBIJlU2mFS9KSpKLJ7j5S1QMzePtNl8wYWneemWpR0k8Ko5Am2Ca6zcJMct0PL5tCjDDw62xsS7rJJ7Lxv9XblK4tL73KM/aZf+c9xaN71XBJLC+10rnz89Jb/8smfuIkr/rh7zGbsrFUhHe3zUjKA5rwf+KvzS37dv+Jp+5wxBq7XK6Zdg+QGSYIGpW0jF50l1VNn732ZHbhmduGQLtMXn+51M9H5SMyOIQWzAVxEVtMuCpLovW0bIE6+eMPb5/b9xNP1nil59pNpMgBF7E5I3pxXaDPN2cjz9Zaf9bcMOVjiG8Md16caCUNenzR7puC4WB14ef6Icf/9roYwFDepMd9BwjexY5s6WhfJjJY0S6R3Njbtcssm9cTsGbK9fsweL2ZXW3u+QxHsrWvKJRJe2c5BEo2kwrCNeNIdJXMFDhpQbThzA8/CjjMZOWjgOq8YCspdqe+71PIiXrDN3QzoBclsU8tVXLONrbXa5GPuscxBqh1wkDI+uswjPdD5NLeh1NXVLT2H3jOdf8fj/31O3reOb1GRXybfX7WUvH+h3n9dpaW7kmy/LZYoeU2O3oj3aVL4AUOLGmcWYdTAoIEp+/l4O1EasUrWkKxyN+SGmCayjm/dtmTwVVV7VLzqrLBeXnF3X2CxgPjwT0AdiOxfLXQf4RADu6kpfpx1wWlJsbu/Aak0peM1K1CqfceE+/hZd9XSc/nPLLimtUhe0HIxypCghLnPnIK8Gh1douJiwkXFTRmnJU2XBVFe3iwTvPch937q01nuWGnNVPTyOEcuP8dDcNyGYA4DPptGwMxbX7wPS6jpDDn11Zs8ZJxLSBvJUYnBk31hRwQ1unMjaFv8nHP6Vr2572NUG51KVQeovYozVXKJBt379c62tCBzs5XfouCBWs92UV6fP0qYbZIsebcqoOTaK16vB7tJFUVjgmlCoz8WFZb7o/V76bdDqL9PfNPPeUAd/U6cC/IXHs4r2mHMl3Fs2N6sGMeADBF/M+DGr05j08oTn3XkVeDRX2b+8smWR+tbWiZe78/Z7Dsury7YX0e43sB296DI57sILUlr5yOtnxhdZJL84yfhMHvbiyuUVzfRSpxbsmp7kqCEkGjXE+35iO8jEQeT2Sj5xuw9Z0Q1i1mL1nlAdE6Sa/3PoWSEXW65zT273DEmo6CnLHcLgO44J8SCtktQQ8J9tD72XACMBqYLiE8hXigu2II7SLbe8pBw3gSQUi46PIGZdfgTmPJP8RVRE2E/CJe3Z/x9/zOGFLg+9EyxtJI6E0RTFaLTeQ34cjgjqfDF/hFDLDo6BaSpRXFfFfoLlTyr0DSJca1ILH3ZQIwO55yJChZgqX5O9bAH2Iwdf9LHeHfBPjamgB49OQl4E+vURmf6ua8FLi8cxoahU1I0RfXZrUOF3dRyHSyZjdnN91ZWZ89hVO6MMCRL1Kfkubw5I+yEcLQ2/25xZSj8dDVx9bpn/9qBU86akXU7zgQKBUYNXMcV+9RwKGh5tfmqtG5qga8k2tVFaRnHxDgRJJUCozdAyOmsZeSobcPWs50xRoBmoZM4J94T5iCxyy2TWj4yamknwDNkZRJlyr4g+omkZl6d1Ng8OdmCIKsQCxMju+O1s5eG29jZdeSKd7pLeDVtFP/21Ocr4/1CwkvUHu5v2gv+NmTco0XO+OvfU6vDNSHPyCwQcAed+SmHmACGBmHynp12NLFnl1tSQXMqHf3cH0hpxXbquB57ZByQPCBvoQ26CGEL3TW0valqNy6ZyLw0aL6H8Koh8bUK+r63uL4tFJg9WbFFlRdTur3Zr3h1OKPZO9Z5Q0u95HQuClWEob7Py3HAq0m4idgtEfM3U/Ll/tTJZqkO6UovVnZC6+NckFJn1OfYC2GX8ftI2Cea7YRPeb5vjYWnc2L/wWTiszp2eSz3+9z/G7ExoKprC6ZqXpPybL1mbySP5UfEFqHSJrw3FHV+bRJIVuhIKyW1SjgI4VLw+zKhNwfC2chAy802EKMHb/srWckrh65b9KxcAf5Dk0pfxOKY31GjV3Axw5gXfZHlTw7mohM1mdTj8a9Kwlp6wst5UymJdTQ/DRrm6yA3QnYmxOdHLa8T3KimvxSZkXRUTbBos0fPBNV07Luk3BOqSMpIVPihLcq+y7a/6j0iyF8E/P95hfxN4NwP/Ky/4cyPvH7Z89u/+5jtq3PCiy2r//gl7tVXrwzjs47b/90nDL9Y8/NfveL/9Ot/4N989Irf3D7h//r5X/Hb7WPknyLyTzv4/QZSQg7fcYXztq9a7J+CTzxqDpy1W7YhM0jm3RBuv92+5CDkTnBt5iwMPPE71m4wWid2/TRidO++Gzl/vuWivyUhDOrZ7QOrZqIPpuDssUJeToKIzOJsIszCb8aCMhZTVM9lPGck8GK8YDt2DFOwHmhvom/OZaOLulI8HoMRfLrMxXrgoj2YeNPUMeTAtMrsfgGHc+HwMyWsEmuZWPmJrpvo+gkfMlEtwZjEM/WO6UyQ0h5yip9m+CKw3tzC7Z/O+J93dy2eQpt49ng790BPBZ1UFf7z1Uf8Zz5iiJ79rjOvei394K2irdI3kYvmMKt7ZxXGM8+L54FhbcJ6AkxDIGdhH9KccFUKeszVGcAxRc/nw2NyLJ+VS2aaBGmV6BVdmSAhmLr5qtyLAF88bphCsLazxoTnsgqvd0cf8lUzzW0isVDcDinwelgzxMBubLjdrEiTQ161PPoSVq++X4++/O4zAHa+4+q3T0ja8/Hja/6LX/2Bj89v2KaOF5OhyrvYcj2urFWg+KhP6gz9LhZyiCXZjbcEe1VQaFc1LQr63cx95ZSWR4dHidjjciYywTZLsGv5UCj0ffWzpeOUw9wQU9mbU4Y9jU1tclSdv8PiTQY0xiyk5DiMgSl5vLP1uXe5tEg4Gh95FA78an3FWkYaTTQHJWy/21j1Xibh8DAN/SFE/H4yff99S4G2+2F1kLdHWmQQH0pv6/eJY3IBSRyjegY1tUJTBNR5MdCK3XRj8uxjg0+JVt9+TCsSng+milmR8FlEZv6H+f+zZ/VP4ATURPq4MAfNMg+wfQz0lQa1SGbvi6o5mbdkQjwcLR9cEcxI8iZ7YEZDCgYyU9MLElHbDlSMCeHvINuGDOaKDsaMGxJuSricF4PjAwj4h3LaqijYvR2uyXJN2u4Isi0vS2Wu1M2ia0s03BUkyZXzqov31HwxKATQEWQCfxCaqHQu0TSR3HjE6+xxWsHdHMQUeRtFg387Dfc9D1GO1HFdHktDwjVXVE+MlivlJMxle11sS1ki4HfGl/m86ZyoH99YzrcvNF6YX+My5EhhLxTRI9TcIabJevHvJ7T1XFckfMGYeK+jUvkvHPI3Afe/amlD5GKtPG4S4x8FhjVT9xgUcvf2ZUVeBYZfrDn8y0eET2759ZMt/251yWbXc7U/5zdXn7C+vuXZzQ3rm+0P9J2Oj07MamvlR6KneMD+uPeOljk3e0E8NC7RyUQraUbGrOZUGEo+0awmOkZLRPYdY1nIo0bjFKlMm7vzTS3Szi4c5SLMOPa5hQS71NqCNtlAp4Ih4U7nljQtiJGq6Wm0ElmHEUnKLjU4tWRjuoDBQbwAaYyeGlwi+IQP+YiEFyHcXJDwfELCf9Lhil6HH6C5FSZaG8uDXaypMC5WYSJmh8+WBB1iYHtoiZMBZJoWCXHRU1Fv90jrE1kzrjAzVs1Et5oYi7aeJiFHh3gtieWbFPRY1MinMZBuG2S0ecbaJnQuxuONsTbfr6Ksg7kHrJuW0EamaP1OlZGCClO0htguHEUS58/Geq1vh47DFDgcGuJtgwyO5tbRbJVm9z2T8OuChF87rl73HJ484swfOGPkeXNLkMTruAa1ZPh66hmLjtMQLYltXOJMlMYXantBwqt1ov1ehc2slzoU0CbiOGjtzc6gnsyyrQXGHNhmo7snjCEwe33rMemua1hfPt9JnoswqdDLe29FgaSWUSZVcjZmYgWmYim65MJM8rUwIxByQ5BcxPoyriDh7jtWbt+rJFznxniHK4ne2+LrEvD5+TLJpG8wsZoau765/bfNyz8xj3Cbt6XYHh8v8tnbz1VVw4RTnQU0NDnC1xU2CjHBTeWirYvYGeUu6O6M9lZk98PqL35rlGRrthRT0NGhg0cnN9OSjxVCG4QOMbCPjQ04ddLhqKBpCHkRxannrSAdyzgm4ceCgFZkhGMfuCK4JjOEhuAyE/7o+02GcYT9BOMEOd9Z4C1zyw8lAVeB7G1BmQOzlQlg9PtR0MYq7Xm2OamJuKGsRiUp21sIC1WhD3EZfMZXETK3+Hu2JD20yXx9D46QPX50MBptLiejrCmCiiK+LAQUmGlu8vbx6j2NWRW9WsOMEFB8FMR7pGnIXUNae/K5W1DC66KmFk903p7Ux8pwuHdcarJvFPeMqj/S0BfqwXeKAXr8fRZ3c4v7qjpD6N3PYfl6+eFrJOn5xd3P5/h95sec5/+blkcZj4ySVL5POZ5qFMmcDal46g587DfoquH582sSgox73LlD+q6IOJmqsjaefN6hTaD/2PNXH7/Gf3zDzx5d8VJb/u7wlE+v1+TfR9afbek+PeD372he/SqavSwKkIXumB4SqvoxQizpzA0cnOd1WhPGC1ZuIgaz/DGhokQgc5t69lPLYQrE5Mr8fFy31P7GueBXv2udZ2tSXnQ86twe1byIRw2Fhlso6+Xarpe4FiSw/kg29KtzsVgLGdLkXbbCoxyZhYMGVITzduCjfmuCVwWFPATHgDL9ZCb7U3xV5MJyUCelX/ooSIpTNDkalzlvBg6xKT8GVhx2LXrwxj4r+kLFyczauQp1fSw95dUNyUmxPfOJNHl0ZzakqXdsCurpxGz8Kg1+ZguqfZYb7jHM3JFirsnNQnJOlJi9iRdmm7uJUphT5Z6IMNIwjYHBZ/ZjY+4ITeT52kTAtlPL9WbFtG/Q0eH2HpnsmOUAcfVuGG9uH+n+tAVV9Gbisr/Ab4S9bzj0LTmYFsWjcCB7YcyerXRM5TuCJa+CZxebQv1OTMkXavpRXXwGjIqAtjGOzakq5GMfeF2PRvUFtT4qeNX1biXLWPpQNaUsCa9r4Sr4PIu9SWbMgYE6zlkLZqsJFxRaiH6ydVgZm1ofedQcaH2icxPbaIJ0N7ct+sWe9tPvdh7emyS89uwN2aq5X5eA1x7i+rh8fUIwGXOLWg3x6BuJeJWZ/6rPmIpNQaXVzjYPNb6uj+5Di2Jlod56h6tPaVJnF6S3ylbnTKDBk5jGwG5o6aaJNr99ZekS+L0im4w/KCRm25T6I5QJXSINqSBZfBio0dfE8muYkn82u4ydJ9005G0w0SYMmRjVcUjCNrZcDytup86qstFUQykgH1qMxkr+NTMH6gGFGVmvvz+0b/PrysJt7Dy9j6hYv+AYHKkFT4LbLVzu4WaDxHhE61VKldEQ9WOi+gMc0HcY6o1qH9dCWkEOxRdWBT9A2CtxBXpmSA2uJuMK2WybJBb7k4JsWSJXhLxQfEhIOyENSKjCSmI6fBGaPnF2caDpJmRscFOPbBryBRz2DXkQpikQRQoVveyjKPkmW1+4lzfFxD6EKMlv9oAKzU7RPTR7wYceWWf00Yrhecf0UUvYZ/rLCb/PJcF2iDtuC8qx9aUwUujHNdmfxdWSIlFN5bt8fm4KSpjr63SRvDK3HRgFUsx2zINohpTMqmgxYFUqPLXQWAoCP6Qw2/Rvf1U+u+x/eXRjSbynhDtMpkOREjLFsu+KxAia0ZQN3c957pXTFGhJ/HXzmr/pX/HxRweGf9PweHfNYe25+vuGw/VjdBzJmy3kiXzeMf31J+Qna375N5f8b/6Lf+QXf/2avXT8XX7E/3D9jNe/b5j+/Z7nf/8lbjsRLt8hBf2h+bkUq5I4Bg343DDmTNa75+7HCPWQemE6E26anv88fMSLTU/vJ540+2LXE3kU9rQS+Wx8wuv9GTdba2zVik4DUxGWmtTuBxcS4grtMx+ptpWG3paFMcAhNxxyYBdbYum9RCyRr+9RBM2QkyOP1hPrWljLyOOwJ0jikIxaPPlgSLdXohM2tFznHnXCX5zd0LR5wZ+Cmzazcxcc+NFPwSl+5Ehd+cWVgutyreBMTG3djPyyv+KL4RGfby/YHDr22xb3ZUvYOuvFPVgxNq5g+CiTzhLSZqbkuJ16HOaAUNHXVTvhRLkdA+0rT3sjxLWts+K6wbWJs/VA10RSljkRz1HwO0fYlvm8aILkIMRzS8RTMPp6RdTHYuE3JE8aPDI4S9ZDtvl7cqSdmLK6Qoy27dtHCX4BPz/PXO1WxM/WdFfuTgHYTZBWwuEdqWU2lwPn/+8vWfce/VnLP3zxc373SYv/aKL52x3u2cTKjfxstaN3k4mfjefsko0V+9gw5sCYPLupmdmZwRnyberyaWZr1mQcmNettf2yJs8zMDe3XNbk297rKD3n5X1Vsdw++6g0VceXCu4JFDHkMs4JpgviEjTg+p3lHYu187kf+Hl3zZkfuJlW/GH/lJupJ32W0f9wxdnfXX6n4/7jJuFfk6xqoTu4b5jQ5kJhuo+AZ3VlhXSMmog/FGkJST3wGXXS+mczJ8gRDT8i4cwWcvVir9S4lI1S49NR0OIrI6vZ/0zFBqiih/MtWAUZjuqJUIGlDzCxuB93+rTLt842GH81Em72bYcY2E8NKTmGMZCKb6YuUPH5Y2oCXs/Hojd7iYTPO1EfymLOe0MwgksMKdDmyIQjS0k6MMsW9gcYJyS/OeBZEn7chff+/imJcw6GiM/UcxSXzJLKtfV1Wmy0rAIuUSEuerzr9z4eFATr7XY+g8tkpws6NGbfJ6bq2q0mtMGy/VHRUYjRFcEQb0zmSm0vxQK9g4R/mPfKjISrISOi4KLgnEeagLaBuPbEM2eFW8GqeCJFYb1sp/SHS73u5zYD7pwTQ4Ar2l02UKeDGe3WBQJuVa2lLqIVmsrn1NcskfAlCl4/v64ofsDIzx/Zx2U9WrZFRYZk1+sYwQ3IlMxTXkZLxlNR1c4ZIVliDke2THZ4zTzxez7xG6aV46OPbhgew+Zlz/a8Y+g7e0dR6NcmkJ+sSR8/ov/4hl8/f82/ev5Hfj8+4x9uP+LT4SnTdSb/IbL+xxHNGY0/LMOsXgszFVqdten/GfoEdEbChcF7rtKa/eTo88REoPcTKzeRRejdxCZ17KeGcWoQyUbrLomy9Ue6GTyoSPTMelrM0VISk+DSHdrmlP1x3i//3HW8MwQ8J0HLuGdIuFGHG5cImopnuI1LWZZIOJw3A9m5Qi+1x3GhQXKKn3bkkn3UgqibZC5eI1bYbn3iPAxcTdH6sidPHjzdTmg2FFsyNValCsMzICjiDcUeYqDxiUar1o31Ltd7wO+F5kZBBb93pLL8Sv3dtWxlfki0goFL4AZ7TB3kzuYBiTIj4YJZlEVRU3lP9nejrpfCcBLcwRnCnsANgoswCAxTmHvSw1Zor+8W960AwMwc+77hDonuM9PyiLtHvL64IO4vWMUdT3890av1cF+EAxfhQBsT+9yiIgwJdrEpNl+Wg1X8xwSy7bEtfePVmcFhf/OSZxuxJee1riWrna+XypA9IuGNHNXWO4lzPmitl3fHkmOWAWMuLAmOSDjls7rar87xu1yEA8/aLY/CgaiOXWx5OZzjN3v6L29o/3T4Tsf9vUHCH4p6MN/mCU7pK5j/X99bfr+PfD+EhteY1b/vTcBuXsjx3qN53zsWCbjR0Y82bXVQqhexQ8vCt9DS5lXu13xEQZbIcmfiz4UqF0sf+j63DNlubC3q1B8c4eD+9SLH4zf/SSsid7d2JGVwCqVq6J3inHmO26OJ7Hif7yyQlostO2dHuu78gYvPOP5+rErW+afaTXQuMfgMjZZ+PTXhqZSQnPCkWenSBkQtdPu723uf47gQrpQ25sRKok30kiiVbGyhUO3CijiaJJnP4Uw1T4oTm8BzEmM6qENK+ferKPuiGEKeFJL5/qZI8f+1N1Yqdd2Q+bgLHNwP60H9rkKYbbtyI6ROSF1Z5BysUCcqpvi+VnTVkHtH6ouwT50a9HjtQk14F1nvoq9WtS767Pp2CdQ7cuvNd7wY9i4RZLRQJRcJenGNK4tJhwsF3V5+dDk1pqNgi73c2EYkCD+k7lTs69eXee6SbAWleq2GwdlYnEDHBpI39HsKaFJIGY2tPffLlhQ6dGi4dGf8/e0nHMaGl+M5n26e8Wo8Y79t2a1WDB+1Zm31PJFzg/uopf2rCXm2RT9KfM4Fsv+YL28fs/n0jOm2Jf4hottoNPbKMHPlYOd3P/BLtvsqjo7bzYrDlWPaTMS4Y+k88aNEnXPLmHOka+oMNpiGjbXp+SI7VKrjd7zAY3I4KX2Tde4sa36h9s+WJEQyXUHZxxwYUseYg2nAzAXcu8PTscB73Pekjn1quY09h9TYZ1eV6SJYmZNjzMHmdQ3W/12K/NYKUJSo7xetTvGTjKbIPaReSZ35PSO1iC24vePT68c4lKthxe2uYxoCREduzeLMjYAKrugISBZkcmQNXLNmE3raLvLsbMd5OzCkwGEKpX2yMD5be7TrVNDRcdi3pf3LmQVZEtj7N91RyjXqJlso6N5xuO6YxoDzib43YbbtvoNCIZ8XQ6XYjFgxX/VY3HVR2B8aXncrptFE5FJnn5s6AwH8QawgMP4AY+MYcZdbgiqSDqT1yPTZxH4lvH5ywaFfMzaOfOZoGxuP2m4i5yIcXKzFhJpgH9eSrrTABHdEvZfOPhUkiurYqymvG3Ju7zVgyh/zwuJlLsCBsEi09cE6d/2cbWo5pIYxmd5VzT+8KJMzFfU8OfIhoJOgElihHBi52nakLxN+t8f/ZkC2332+eG+TcL9oqn8oHOblWekF82P5mbfzlqT7rZ9/fxJeJhMfItXzG4ZSEEBv6thRHVNBCZa3uinJl56LKKQyYL2BhN9jP4gWQY6oEM3iZMyBMfuSjBs18CauEIGbuGKMwahvyfFBZHN3QgylK+jkjIMvVuoV7fOj4CYxOyxMXKd1md7DwQdaH2l9ImK0JwQanzhrx7m6a/YaFMEKd8xN7mQGi72b11k6VyJNcCMUJcjEmR85DwfGtkXXmelccV0m54hOIxJHGp3oZbIWgpqMu8zsZevhO9yGP2pUSmg8E1Lt+y6LgjAozc4q3nhFO0C0TMaKZGdI9iRI0mPFGhMi1EnRlZiYzAQuOoLKveLI/R0y+zcZEtMA8eAZDw6NzhSPKxob3azfENeO6TFIDITw/ifhpkDuyJ0nroTpQphWRkUP24zfKy47uDgj9z3xo8D01DM8toJIdiWZcraI0+IYcKR+L1DAMhZVKl+zU8JgxRVtDF3XIEVtviSs0VDkqpRurTpSkvhS6O0dCfBbh/dqTK5ieVYXbWaHY1oDkj2uVfzeIf7rxUG/awxPSltLA/Gs9C16h7SGxHhxNM7jpNKLzZ0iRyENDo2lmJdLf/i5oCsPt8I/7j/mZtdzFkbGXcvm8oxp35CvHemjQG4dqY+MT89IfeL8IvKzX2w5O78htZH/Xn7J/3T5c8ZPOzb/zwvGP3boiz3pxcHo72I6ACblnVHiO03EJavR8KfMsAlc/ekZMUP4YsNqmGh+ZH30Ol5kD1JFpVycF51DDvQy0Uhk5UY6iXPfJNnGfZLNJQdvoklx8nMiAcc5IPnjvNBI4nHYcxaMYnk5rrmdOkO17mniiOgR/S7Jtf1NGQi8nM5JB0dUYcyWZKfkYXLWdzsFbuOK1+mMXSmwx7KgHnIojC9Tqr4vyHiKn16sv7R7bDr3pMYTS72tLlPa146r3z3h9eqRsZWiwdQCxLNMPAM3CmkrptfiQSL4jUPUIV8GJAv7x5mXvxK4gP3UsN135iwyOXKrTGdCbgo4NAgyefLgGF0LUfCHo+uJm0rCjhVfa/uTP5iAqt9D3rSoV3ID21VhqEUhDDLbsjHYvaMe0iobo65U/SUbyj5ed3wZPfng8Y0ynVuhIV4kaDP+KtBeO9rN97xJXPkSlcEFuM1A+9sv0cbjusz4P07ELnN42nP113+Be9LT/ezA4//la9afHFiFiadhQy8TvUQeuf08RlnSfezTdhgRIGD93LaM0VIkPP5scsMX6YxNboHjWnWTOj4fH7NNZk1WI2bHIQWSuhmBf5BsVg7XlH0RoLQC4BhDcYBSvCuU+E0gftajm0A3JF7ffEw3JKargel3t/RXr5Btxn3x1Y5QXxfvbRJewxUVu4y8gVD78rf6+zfZ1lsp6V8TH1z+911icRdUxcFcqG2VRl3VUQXuIuH3T8Gc4S0S8ZI4SFY0V7q1zEh4LonkoKVinoOJMWT58FDweyGLX+7IzKncRcIXibMnE4QibFEGNHd8DD7TNZEuRFN1rOdKpQh43fvs5f4s0MEqEieAK+Jf5q9pVMXWJYJL0CipKtdSekkXSHilBVXKUVURf++zQTC0KJTvVpFwMDpyNFTWZSz5rkWFSkN2tecWqq/ZDCQVxLSi2Ckrqo6vq0uYWJvikhrNrRS7SEcky6xSyj4h5OAMSW7ch+ETfh8Jbw0Nr3ZgRyQ8QAjoykR0Um/J5azGU7ZlUHdJxCtSuIyKsmWzHFNv50y9I4vepfZVJDyVRN7ZfpbN2/uc9QSqAwncgwztpxY1K82+rCmQID/ofZF6KY/K+BhjSDQKa6AB7yG1gg82fqfsSu+jI+4DOdbFGXZMnSIhwwhjDFzH3saMrce9auwxgl+BayA+ahh+1RAvMv16R/PRLevVnkMKfDFecDg0yJXH/77F/dajmwndYQJxzs2JuIJd8++YIm5IeCaOns2mZX/tWG0TTfQ0X//2dx+CjTmutnzpbJkEdQ2UCRxbwYBZEwQgZ2tZQWxROiPh80uKqGdl/Ykpw6/dxF5aUrYE2pDsN3dRy4ZqC5SW/U4q7FNDSF15XgqV0yB4SbZvowYOuWUsCbgu5vxUGHGV4XNKwH/a0WwscVEvuOiQJPPYjEI4gF450s7dAcG0UXKfIWRScEjyRvGGuR3MjeafbYrVjvET61UeoydFT54c5IKENwUkUGwfkr0fLKkPe3nYnrSuDwqrCi3I/B4QMTvXUcitHgtK8/xj13jqyvqhtLfV7++SIIMjS4Bk80tuIfUZOYs0XWQ6+Lmg/P1Cjg9lkS1Twr8+2kzm8pM+dkz5nPT8gkfccvG31zSSWIeRj9sNF37PWkae+w1rN1kSXpmzogR0TsIbwIuQValGI8ul4nXu0CnT564ctqN2hENn3aGsxgIac2ATO6bkj3a9D8yvqsexqbbdxGzAk/m0H/ObvG+Ir9fk64bDVhlfJJqd4l7d4v/hmnD53Sjoy/hxk/C3ZFGSlTw5bmNP9BPnxUSwUqHNvsLCLxJze41VQ2rKkcuVvEy4l0n2cptIZip0hrf5jR89cx48q9/k239wkdUUEF0KjCnMvfH14h1ymCvX1J/7h+INJFxxMaNjNl/ezExbm3ulyyQO5a2Zsu0Htv8hhAiIKz32tiia7VhKoeOhhGmaAle3LftLzyZ2bIeOITXk6EijCbglD+PUoj4U4R03DzIVFa+7cHef4HiUgVKtFEzRM022/SF2XIUzUnTcbNboxtPeGooosSwCy3eofYi1CFAF9z6IBJzKcllMtHVBgPVx5SDkSj8Hlv2WUl+/XDzWS7YUInKwRCdPiiTrr5fKEggKjQm39WFiFUZyA1MP08rNVLSHrn+bO2VeJBiaxgdx3CVm3G5EwgGdhNYF/FYMBb+ecLsECCJG3/d7pXttFOr2Oht90LujYGbN1VTfZBRXf/B6YS7OWU2yjfmgR5q/yDGB5vj6WsDKyHzN4MWSR+dwU8Zf7mw/vENbhzqHi0rYZdykuJsDEt8uQPp9ov3DNQCpBXdmbST4DH0GrzifCY0p8SOO7JqyZDoW4ebiQy2WtM4KF86U+UWAsYw5wfQJaI1O7s8y3Wqi7ayAuN92MDjS3pFeB9xBkD9kuDygG0xfItXjoQUBh1m1/V1GOb+SlXN/4OmT17hPEunlgakZfmwy+tEdIYHPlhj3fioetkbt7phMOE0bIh4XlDakWWBN0BlFF2dCSCEcLc7qEXReiWUOH8s8figU9NoC5cV6zJ1b0EWlkABdNmaX2jgjCqFJ8xhYF8rzF2sU6TI02cResZ+afMfsGZKx4aYpIIOJt7pSiDvFTzP8tSUwbVbOWmHauDvJam5gWpf2nWXdOQipc6gzQTa/P7KSoNxLUQkHs45qdsKGNS8uemMcHiBEwY/Q3IAftCTj8oY0lIuKH9SK64u5XDJHZ45F4cB2wB5SI6TeHqu48NzxV+ag1Apx7cmNxw/Q3ih+MMp5c+NJnbfWrLEUslrP9NKTW+XsWlh/kegvvzsKW45oefj6e03GiLsqCupux/AfItsvbG5oQ8vGK33o2KzWdCFbm16gOIkqIavlAE5wjSBO0KSm/5FsX2r372ZyfLFv2U5+TsIBblPDdlSGlMo0X0AnVTRSFkBq7V/35o05rwAr9mWHU8GrEJIxJyooBaCbgPs8opuAPyj+OuMOIDdbmL7vcbd4b5BwiQp7z5+2j3nc7XkSdqxLIl4VuoE5EUy4+XcnShJTOZ/Uk8S9kajX7QB3EgYn1mNV7TOWsVRO/5B6W99VTGrqkvsBpoKOgtGVt7njJkW2qSPFgM4IXXmzXcmIzPX6GVny+wjNhNsncpRCSXNzD3O1Uqk3lyZ33P6HFiKIc7YQF08sWdKYPVP2s/J8TVaXSetm33H56Sek9Zoojp0rqthRkL3ZXYzSknxvC2llRj3gWPG7U15kkUTCnd/rBKHZ0BLNwqYP/HYIhD7Cy4D8qeHsVSa8Uvx4/Jyongn7PlGttWBWaxDu7cN7GmK97rmxqnStzNtkaeqrczKMltqKLXydcwaKJ0y7oIh15WAIpAbIXaGj7xw6Ct4J0gi5y+gqwVpp1iNPVjserfbszta8enLBYRcYHyk5FPfMxT1WaikzAGx91SAPLCjex5Ah4l/cIrcjoXG0L1ty43BDxN0OyBAhBGTVQwi0yXERM7kz5MRPkFtXEsUiJIXOdHL7kPJhWSCq2ZWhNp6Itd3kttLQC/qeS0tBEFTcESyoNNmy+NLO5vzUCr4RaAISAn4f6f/hBTkIEgL0nT3mjIzJ0PWhWPz9QHH+3/wBYBYQnB0K5sKRzn71NA2yXkPToE1DOne2KA1C7OwxtRDPHalhfj9i/e6xhdyDdAnOR6RNhDZxfjbim0Q+BF69eEw+eJpXifV/GgmXGV4P6O826LWJwjGW45HVKOgVAX/XyVhWJCZkjPy8e81/9S/+kZ//7Ws+2/X8d+tHfE7/9dt4l1ESBzdBmzPnbuBJs2czdbyeVmxjR87wtNkhAnsamqLiHFyiD6ZwnrDCeVaHC1rY/EUos8wHLmQTJZpaHMpt7MkI+9wYMu4jKTiakIh6l8qOSGGvFpaUN9ZT30yz33f9LMBEss4i4iZ0nYjO1llTsUEbNbBPDTdTxyE2xH2LXAv9VcbfWhJ1ip9myO//BEBoGy7+0ENYrL9VwTk0+CNduhZCRVBnKuPGskiQ7pXNcjaByZzRtuHjs57cGnJMzEjOkHR2hJj1J5ZMqCrUWW0c4Q5TSpafWancd0V2jvt/P8Gdm8kdBG8tTjHDFJGY7H1NMI2STNnfbK9t7PXuEJHNzuaR7xP5m5ccZTcQfvfCLCd/k7j6/w3c9pmA50t3jpc1bt3RfvwId9ahwZHWprXiYsYPGZeytSude3LrcEOmuYm40Yq3kjKSIe4H9q82xP3x+1W25yErURfPYwl2ysP8/7d9q7kgqXXNILj7IJ8AUdDSAuhS0QXK2Hnavxv3jvcmCScrMjk2U0vj052EeKlQvky+swoJR0NJoinN9MiMjn9VIm6+dLJAz2Xe3jKW6PgHQ6t9R5FVGJInpqJ6WJkHpWAxajA6R5aZInvEVisyVcuXx8WUxAxjRqIWJFzmBcKbtSs53lFV/O1Di1KQOIo/uJniX4tBM3qw+HqGhJ9xuHyEBiW3igbFTWaV4SY7rKlel4sq8v3PX167b6DTy/eWF1T7pTE5do1DU6bbCI82jm6juB1zf5OdnioeVET8SgGFuQjz/ofCUZ21PFGTLvVH73Bd8iSLQvlM5Vocxyq0lJsq9mbKqTmaOFZGcB6YkfCMbzJdmDgLI6lt0d58QFOnpsB9p8hVxFyW38Fx9Lj+AG4VSRnZjWYPFoKhDt6jU4Td3qrNXQsugApe1TQTvBYxNYf6I11xjkV/2/EPFeGe/2t/DRTEW+YWAIlqdmXL66FsRxb93lVESL3M1nDiHEwZv9/hc4Ym4FYJaSwJ15jsMSX7/QeK9o833/i10rW4i4B0Ql4J6WlGe50FkHIDsRMTL+oKmlDHk06hsUQ/d4o+irCKeJ/o2onGJw6DZ7/tONx2rL6c6P4p034+oTtFLw+Ggt+P/NBg9q5CZ9X4M7/nrx+/4F8+/4Lu8SP+runhz5GEF8FSlzOtJHo3sRfz293FhpVvOOSGoSLhXmmaSOMSq2ak8WbLlKMQExQJ4dkbvM6zzikJx5RhTIaEB81WIH4ACb8TcsSSvMuzn3LwabZIW3wlu0aaDNlamdRZe1tCZiG2qGapdkgNOnlkEEMxR350fbxT/HiRbr75+PSuQu89fsjxw80cXx0yJeT6SFMf/lh/c0Dps7o4g19+jFyckVpHvGjIjUOmTNgn3JRJvWN40pA6R9gnutcTfp/KmJytCLvZoZ8D2x0Px9uPwDc5x8tlww+lzfJ18f4k4QJ4pfWJ3ptaZy+RCW8N/k5nmfr7vd2uyteji9/znFDnReJWn1uaw9fXH7ThdTzjkBsaSVz4gz02A6GLTCtfFn1/rtP1w8dMm20F3yh9iLRhZMoO1ZYJaIqPuwlw5WPilxeJHFZdsn/vTs5GC1VEc1H/zuDAa0YXi4D6U9FG/JuWA+99iIDzJjLk3YwoVOVGQYt4maKBYnVk39EdIt2ftmbF5ECb4gkdBXcQXCoJ7qwQzRtAd336Tm1pmYzPv8ndJLxsS9tMOs9om2muhfCZw10L7nprVeQ7G72XcM/fy0TO3vv6iVfoMrrKVPsQ8pEKp9XDdFnsrv+t/e+Vmlyq9C4pzdZOSjxT8lqt91iO26WKySTQKbDpLQHYDz0x+xl1dN6siIwZYn3ltR9NsuC3iXAVaa4y7mawxPY9D9WMxGgjRM52rXtn4lxThBRRDdA5dNWAE7QxdMHORz2OcjwvJSkuJ+Z4vZdiGOVp9QuadaHX1vOMP9LQZ/SjFsqrx7fWfvDyGd5B38E6QYywMySGrDB/R/uemsvf3pPhTHOxHATQBEFgGJAguJ2HIIQGdGOI/7KQlxvwhe7uukT7aMB1xZ6qmYy+vFXalw62B9rLhLs6oLsIw/CtkJh3Fimj+wEUNrfw28uP2L5s+Oy6Yzt1X//+dxwuQdhnKGKEKVr/tJPMs3bHyo30ObF/1fMit9xMPdO2R6eW7DND60g+k5K1KuXk6ixjxV8Hydtj006s24lVMxJcZihiRmP2HGLDmI3+ed4M9H660zsJzP2Smg0lmpJHvGN7gMmn2ZZSBQ5jQ94FZPRMruHqsDY3D5ReIqsw4TVz7XoOYuwEvdnBK4+72f2g7RqnOMUpfoBICXZ7FEWCQ0aPa9xsj0lUZO/wk0cKEs5thCGj1lNp8+Kd9qSfbrw3Sbg6QZvM4/bA4+bA47Djsd8yaaCRjoTQy8TH4ZbejbMFGTB7StckvJfpzt/vRypIbsLhOb7vKq35nfuY67Sil8iF39NI4mV3zrPHWy6B8cLoIHMs6Sc/gd5wU4g29KNfZx6vdlz0W/apwaEcUsMqTKz9yNqNdqyzJYZ36OjA3ESWS0YCM5WTlEvFP9L4iC+9zFUopnOmtt36iG8S0mYTBXrfE7l7IWIUVZoWKT0ws986R8RBGyV1tpCtyWpzOXD+//mS3Pu7lO6K0GZ5+Hg8gIR/dSy4zA9tw+nRC3uCsBNkApkishvnly4BxhnXLyhw6jFxkvuoyvsWQeEiwtMRRgfbgI4lUauCbctiQm1eqrTcAFKputG+rh+sdzmMyvAM4mOI59hxLUm7GwX/hUdwpMc9n8sT5HGEmwaNjSV8XvFtgi6hkyeOdq+5CcLG4UboXkRWv72mfXEwqtQPSHV+Z5Ey+XCwcUKs99vQuzoZmy1ZOm/Qx32hIpZkAI6Fv8XQY+jbXRlPra+vLAVvhcY5ga6vEsiNg7BURF9sSGofuBT6P3OBSdsGnlxA7mC/h2myZDxn8jDAWKsBtcLFnycBfShiIu92iBi1X25vDdWX0rdXeiGbB3xprfBUWjic4ppSOC3jnIoSkuNiuCEnwY2K22TyWAoR76i37lvFOKFX16hzfP6nhv/bb/41zjfkPwzE/TXwbqiG3zTcmOkvE1knwjozHkxkqJeJvzl7QScTVy8v+Kd/+AXXr86JybOfGnL2jEEZeyviSgQ5CMTCxPGQRcgdTBcmqnn+KPH88Zan6y371PLqsGaf2jv+4Cs/8cn6lpW3PvSbsWfI4WidJsph03L9+hGHbcsoyr5Q001Eypg7efKkXYDJsRvW/L77iC/jOc/bDf/64gs+are8kHNej7YPHCb49Ab+k7UKsPtxz8MpTnGK7xnDCC9fm/KngPc1D9C5F945oamWnhlcLMAHOk+PpGRz6E883psk3BRB1ESJvMnct8WcPWE94Ws38JHfsHYDHp0TbSfH3xvJ9EVk6qsiA5NaMm6+m/Z8S+YynTOpp5eJtRtoq4VHO7LtWw4m6Xdv3x1vSoN/oFGR8EaQJtOHyDrYRLj1LVG9+UG7I4PAKUZTfCj5E7kLj2M0Uclm5ePuI+HZfPpCed6Q8Iz4KoL1nidy96P0hEsRjyqA2wIJx2jFBTHWhfCXOyS6z76KivN+hZ2VhYI+HL+XLwv1P9/ufbNwQGfCVSrAvvKVF0j4GwWNheVc9Q51x/dIMhG7ZptJvSHWd+4LESSatYmLQpTAZtcT20wYHJ06G6TFBJUImZQW11E2r1A/CH6XCVcDzas9GifyB4CEm5pgmpllD10jqpncevLqrmZ17d/W+/3CIjMSd3dD9Udn2n4OcqQCl0/X0vOqNfl8MOm0JOeOn7x3Rp1fO6Pf+oX1y/te0VeFKR6P/2FBLHgHm1+QFd+PyBkO1j+4uW24fP0R+1cXrG5u+Gg6sPqRk3BJ4A8Zv024vZKiJcTrMPK03fHI70nasr/sefXHJ8dCrJZWgZXZLPkJwq7Y7nlml4e8guQgJUV6Ye0mHjWH2f7sZupm2roi9D5y1ow8ag/sY7Te8GQstcaZDaxKQA+OuCn3ZR0nPaZzEUAmwe8FFyGGhuvDim0Tis3axBO/Y++Neegwxojc7uFyArWWjVOc4hQfUKQMO2svmpmCD8RPl0/87eK9ScIlZeTQ8sXugs3UkRE+DU+J2bFJHTF7Vn7iN+3PWLtx9gI/JuL5zu9vi1yE3moSXt9zHdd8enjKNrZ0PnIRjI7+n7cf8YeXT5huOtbXgozx7mLxp5KAY4uBsM+wyeiN4/Z6RWrhkBu2U8+QArdd5kt3wdAGLjdn5GtPd6U0G6vEz1EWvHcOVlZDIdxAPCS2h4bDwfqIY+knPuTAJnUIsJtadB+QrUMOxcbiAwrVbP2tQBoC46En7T0S4aVGdqHlanOG3gbaW7PVkA9s3bGPLX+8ecbuMrHRjlf5nI127A4tbB3NBsLeKJfvc7hdov3dwVaSo4ONh9E8SJsN5uO+NspoXCmudbgzD41DdhPycoKd4tTjNSDq8NuIez0g24gH+rXgLsEdhHDtcIPM6qcugbs1ZDCdKX4rNJ8H/MaRNwkXJ/QikSdH2jfkyRDwUHxSm1cHZDegcbJe4w+dmbOwOKx92MCsNLsUkKio+LEnY9Er/4Yojj04l60oUtk5iiHcJfm2JN/eYMr5RcQllyG/1FHUmTe8GzMyJYgJSfnN4sAp3svwu0j3qSn+dp/ucPs/w0CVkvU+qpIvE4fPWmLn0TbgL+B1O3B5eU76MtJ8sVmgSkr2guuY7fbCQWf7vNpy4TqQKyV34M+3HG4St+dwmBK6H/C1E6GKeYaRYZXYNcqQMnGa0KxklOQyKopuDjSfb+g3052KjbqiX+Jsf2yME/xZormd8GeJ6anjS31EfOK53J+zuVoz7DvcjRJGwWmhpp7iFKc4xU843psk3A2R9tLxhy+f4Vzmd90zvMuk7BiHQErO+sJDmsVClv3B1dstZyFGf/TGXMAhs+x8FQJb9MAC1ms5FRXukHFdMjuP24b+jw39Bs4+y7ib3VEv5Cc2UfhRjRZHJMfAZ48+Im9MaX7CBFU2/YqbQ0fbTcQXHfkPHRcvIHxu3o5zaDYq+rJIESPcbuEwcrhquLxaE68bvMu0TcR7O4deMre+52p7TnzdEV43uCsP04eVhM80T+cYbtbcvH7E8HrNtc9ctmd4l8ivW+JnHevX0LyUWXV8jvv+Yu/VNSdc7s959cen6EVvfetrRRtI1y3yIrB+qTSX78LP8oeN5sXExb+/Rv/brSVQo6LJhMBcEvOr9kpuMtkpsu5xzx/DqofdAC82sBuRtkMuLpCuRTYH5MsrZHswfbHfGnIlSex45CoGhpFEgrLqjRVRe/8lmf2TrrLRpLOg1Su8WKWQBTcm3GYymm9Rdf1go6jVSqGoSzK7GNRE0yTrMWEWynHIb6hoz1ZjVbnWGYJNNOaBlgSfIrSWGyGtwpxYmz2NGmreyOz1bdvOlgBN1oMeNhm3m5D9iA7T3eNfRSrvCMad4n2IcDnw6P/1gtx73D4RLocffR/kMKBfXELwjGnF4elT+GKFnid++7MJzjLy24j7/+5Z/+72qDOQc7kPmHUKJBXl/sq4oaDTRSG/CYnrfmQXhKgRTbd0ebvcG0QyNz6yc5mkEzHnIiAKufaExwNnw5Y+3sW0tOhjzK1TCVDB9Q7/qMV1jsNfBf6n/+MvkH8B023H9rNzxk1H8+kt6+0NTUXAT/fKKU5xip9w/LhJ+D3P6Dt/ihm/F4ZNIDmY9g3iFI2CDGbHlERJ99WJ4Q7nQZIgoxzRxEUSPr9PxeiHlfm7SMYlUQR3PLkLpADtrdC/hPZW6V+n728J8B6HJCXsFd1mDp1jc73i4Nxc4UZgjIFD43A5ETae1U2guwa3vYviVtr1nbOVs/V5pEwcHNuh4XDoaULi3AlN0bvfxo6onv3UkvcB2Tnc4Ay5+pBCy2IJSEPH4RDY7zvEK5upRbzSbB2rTaC5FcL+HpvgIV/6t9xHf47YTy2Xt0/Yv35E6CMrtydoxA0NbmdIuN+//wi/2yW63x/m9hJNCzTZLVaVNbk6z8jmDDnPsI3oi53RsNYRed7CysF2IF9tYbunCqGf4puFmAccUkQDtSTKs4WYA23kOIjnI1o+J8q5PK9FDG8WbCuFj9oek2py7Em9jVyi5j8ryVwc7P0UVJ4jgi5WnHFjsb2KCVKyfT5+Gagice/RvXsK8PuE/+Ofue0nZaRQONPKM3zeEvOa8TFsgzJNsL7c8NGLDesvNiakV/QGvksMwFDHM8YHF4IjtTNeqYofC/IJAA0Dzf03fkVI0yBn9nig4eq/PGf4qIFNQK465DbAzYE8vV/z2ylOcYpT/FDxATjJnuIUpzjFKU5xilOc4hSnOMUpTvHTCNFT480pTnGKU5ziFKc4xSlOcYpTnOIUP0qckPBTnOIUpzjFKU5xilOc4hSnOMUpfqQ4JeGnOMUpTnGKU5ziFKc4xSlOcYpT/EhxSsJPcYpTnOIUpzjFKU5xilOc4hSn+JHilISf4hSnOMUpTnGKU5ziFKc4xSlO8SPFKQk/xSlOcYpTnOIUpzjFKU5xilOc4keKUxJ+ilOc4hSnOMUpTnGKU5ziFKc4xY8UpyT8FKc4xSlOcYpTnOIUpzjFKU5xih8pTkn4KU5xilOc4hSnOMUpTnGKU5ziFD9SnJLwU5ziFKc4xSlOcYpTnOIUpzjFKX6k+P8DUiREWXwdD18AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 10, figsize=(10, 5))\n",
    "for i in range(10):\n",
    "    idx = (train_data.targets == i).nonzero()[0].item()\n",
    "    img, label = train_data[idx]\n",
    "    axes[i].imshow(img.view(28, 28))\n",
    "    axes[i].set_title(get_fashion_label(label))\n",
    "    axes[i].axis(\"off\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初始化模型参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[-0.0067, -0.0123, -0.0016,  ..., -0.0049, -0.0209,  0.0104],\n",
       "         [-0.0089, -0.0068,  0.0089,  ..., -0.0015,  0.0043,  0.0119],\n",
       "         [-0.0059, -0.0122,  0.0066,  ..., -0.0158,  0.0006,  0.0082],\n",
       "         ...,\n",
       "         [-0.0017, -0.0021, -0.0073,  ...,  0.0060,  0.0107, -0.0038],\n",
       "         [-0.0100, -0.0054,  0.0018,  ..., -0.0080,  0.0032, -0.0165],\n",
       "         [ 0.0164, -0.0110, -0.0006,  ..., -0.0017, -0.0012, -0.0044]]),\n",
       " tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_inputs = 784\n",
    "num_outputs = 10\n",
    "# 由于transforms.ToTensor()将图片转换成torch.float32类型，所以模型参数的数据类型也要统一\n",
    "w = torch.normal(mean=0, std=0.01, size=(num_inputs, num_outputs))\n",
    "b = torch.ones(num_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax(pred: torch.Tensor):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        x: batch_size*10\n",
    "    \"\"\"\n",
    "    sum_ = torch.sum(torch.exp(pred), dim=1, keepdim=True)\n",
    "    return torch.exp(pred) / sum_\n",
    "\n",
    "def net(x: torch.Tensor):\n",
    "    \"\"\"\n",
    "    x: batch_size*1*28*28的四维矩阵,首先要转换形状,再做线性变换\n",
    "    \"\"\"\n",
    "    return softmax(torch.matmul(x.reshape((-1,)), w) + b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实现softmax回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1, 2, 3],\n",
      "        [4, 6, 5]])\n",
      "tensor([[1, 5, 9],\n",
      "        [1, 8, 6]])\n"
     ]
    }
   ],
   "source": [
    "# torch.gather()函数学习\n",
    "# 输出结果为二维矩阵"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "损失函数和优化函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_function(pred_y: torch.Tensor, true_y: torch.Tensor):\n",
    "    \"\"\"交叉熵损失函数\"\"\"\n",
    "    return -(true_y * pred_y.log()).sum()\n",
    "\n",
    "\n",
    "def sgd(params, learning_rate, batch_size):\n",
    "    for param in params:\n",
    "        param.data -= learning_rate * param.grad / batch_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "开始模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1, loss 0.787, train acc 0.747, test acc 0.793\n",
      "epoch 2, loss 0.571, train acc 0.813, test acc 0.813\n",
      "epoch 3, loss 0.525, train acc 0.826, test acc 0.818\n",
      "epoch 4, loss 0.502, train acc 0.831, test acc 0.825\n",
      "epoch 5, loss 0.486, train acc 0.837, test acc 0.827\n",
      "epoch 6, loss 0.474, train acc 0.840, test acc 0.829\n",
      "epoch 7, loss 0.465, train acc 0.843, test acc 0.828\n",
      "epoch 8, loss 0.458, train acc 0.845, test acc 0.827\n",
      "epoch 9, loss 0.453, train acc 0.846, test acc 0.835\n",
      "epoch 10, loss 0.447, train acc 0.848, test acc 0.831\n",
      "epoch 11, loss 0.444, train acc 0.849, test acc 0.834\n",
      "epoch 12, loss 0.440, train acc 0.850, test acc 0.836\n",
      "epoch 13, loss 0.436, train acc 0.851, test acc 0.837\n",
      "epoch 14, loss 0.433, train acc 0.852, test acc 0.836\n",
      "epoch 15, loss 0.431, train acc 0.853, test acc 0.835\n",
      "epoch 16, loss 0.428, train acc 0.854, test acc 0.835\n",
      "epoch 17, loss 0.426, train acc 0.855, test acc 0.835\n",
      "epoch 18, loss 0.424, train acc 0.855, test acc 0.839\n",
      "epoch 19, loss 0.422, train acc 0.856, test acc 0.840\n",
      "epoch 20, loss 0.420, train acc 0.857, test acc 0.840\n",
      "epoch 21, loss 0.419, train acc 0.857, test acc 0.840\n",
      "epoch 22, loss 0.417, train acc 0.858, test acc 0.841\n",
      "epoch 23, loss 0.416, train acc 0.857, test acc 0.841\n",
      "epoch 24, loss 0.414, train acc 0.858, test acc 0.840\n",
      "epoch 25, loss 0.414, train acc 0.858, test acc 0.842\n",
      "epoch 26, loss 0.411, train acc 0.859, test acc 0.840\n",
      "epoch 27, loss 0.411, train acc 0.860, test acc 0.841\n",
      "epoch 28, loss 0.411, train acc 0.859, test acc 0.841\n",
      "epoch 29, loss 0.409, train acc 0.859, test acc 0.842\n",
      "epoch 30, loss 0.408, train acc 0.860, test acc 0.842\n",
      "epoch 31, loss 0.407, train acc 0.860, test acc 0.842\n",
      "epoch 32, loss 0.406, train acc 0.862, test acc 0.843\n",
      "epoch 33, loss 0.405, train acc 0.862, test acc 0.842\n",
      "epoch 34, loss 0.404, train acc 0.861, test acc 0.841\n",
      "epoch 35, loss 0.403, train acc 0.861, test acc 0.844\n",
      "epoch 36, loss 0.402, train acc 0.861, test acc 0.844\n",
      "epoch 37, loss 0.402, train acc 0.863, test acc 0.844\n",
      "epoch 38, loss 0.401, train acc 0.862, test acc 0.842\n",
      "epoch 39, loss 0.401, train acc 0.863, test acc 0.844\n",
      "epoch 40, loss 0.400, train acc 0.862, test acc 0.841\n"
     ]
    }
   ],
   "source": [
    "learning_rate = 0.1  # 收敛速度慢，尝试增大学习率\n",
    "epochs = 40\n",
    "train_acc, test_acc = [], []\n",
    "train_loss, test_loss = [], []\n",
    "for e in range(epochs):\n",
    "    train_loss_sum = 0\n",
    "    train_acc_sum = 0\n",
    "    train_length = 0\n",
    "    for imgs, labels in train_iter:\n",
    "        pred_y = net(imgs)\n",
    "        los = loss_function(pred_y, labels).sum()\n",
    "        los.backward()\n",
    "        sgd([w, b], learning_rate, batch_size)\n",
    "        train_loss_sum += los.item()\n",
    "        # 梯度清零\n",
    "        w.grad.data.zero_()\n",
    "        b.grad.data.zero_()\n",
    "        pred_true = (\n",
    "            (pred_y.argmax(dim=1) == labels).sum().item()\n",
    "        )  # argmax()输出结果为一维，每一行最大值的索引即预测类的索引值\n",
    "        train_acc_sum += pred_true\n",
    "        train_length += labels.shape[0]\n",
    "    train_acc.append(train_acc_sum / train_length)\n",
    "    train_loss.append(train_loss_sum / train_length)\n",
    "    # 测试集预测\n",
    "    test_loss_sum, test_acc_sum, test_length = 0, 0, 0\n",
    "    for pred, y in test_iter:\n",
    "        pred = net(pred)\n",
    "        tlos = loss_function(pred, y).sum()\n",
    "        test_loss_sum += tlos.item()\n",
    "        pre = (pred.argmax(dim=1) == y).sum().item()\n",
    "        test_acc_sum += pre\n",
    "        test_length += y.shape[0]\n",
    "    test_loss.append(test_loss_sum / test_length)\n",
    "    test_acc.append(test_acc_sum / test_length)\n",
    "    print(\n",
    "        \"epoch %d, loss %.3f, train acc %.3f, test acc %.3f\"\n",
    "        % (e + 1, train_loss[e], train_acc[e], test_acc[e])\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可视化模型的训练效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MGcnsimJmVxQztbyQjHRNHCzHPgWCDEntXd08v2Ynv122lefX7qSz+/C6RotyMykrzGZEQRYnjy3ig6eMZU5FMaeML6Y4LzFz4ogkmwJBhgx3Z1lVA79ZtpU/vL6dxrZOygqz+cRZEzl/2kgy09Pojjvdcacr7sTDz93xOFkZaZQV5DCiMIvh+dlkZegdv0SPAkGOKfG407y7i4bWDupbO4i1dlC/q5NNdbv43cptbKprJTcznYuml3PFnHG864Th6s4RGSAFgqSkhl0dvLm9iTe3NfFmeDdtTXM7sbbOfa7a6WEGZ58wnJvPn8zFJ4/SzVAiR0B/NZJ0dS3tLKuK8cbWxiAAtjWyrXH3nudHDcth2uhC5kwooSQvk5K8YF6ZkrwsSvKzKM3LYnhBVkIXDhGJAv0FyVHVHXfW72xm6aYGlm2Ksayqgb/V7gKCO2qPLyugcmIp08cEUylMGz2MEQXZSa5aJBoUCJJQdS3tvL6lkRWbg5P/iqoYze3BXbfD87OYM6GEq08bz6kTSjh5TNGg31ErIgOnQJBB09bRzaptjazcHGPF5hgrt8TYXN8GBO/+p5QX8sFTxuxZSnAw5tkXkcGjQJB3ZFd7F0+vrubR5Vt56a26PQO+Y4tzOWV8MdeeOYFZ44o5eWyR+vhFUpz+QuWwdcedl96q5dFlW3lqdTWtHd2MK8nlM+ceT+WEEmaOL2JkYU6yyxSRw6RAkAHp6o6zdkczj63YxmMrtrKjqZ3CnAwuO2UMV8weR+WEo782rIgMLgWC7NHa0cWmulY21bVSVb+Lqvrg8eb6VrY0tNEVdzLSjLlTy/i3S8dx/okjNQe+yBCiQIg4d+flt+r45WtVPL26ep85f4pyM5kwPI/pY4t434zRHDcin/NPHMlwXQYqMiQpECKqrqWd3yzbwrzXNvO32l0U5Wby8TMnUDmhlIrSPCpK8yjKy0x2mSJyFCkQIsTdeeXt+qA1sKqaju44p00s4Zb3TuKSk0er+0ck4hQIQ5y7s3JLI0+u2s5Tq6rZVNfKsJwMPnZmBR89vYLJ5YXJLlFEUoQCYQjqjjtLNzXw5KrtPL2qmm2Nu8lIM86eNIJbzp/M+2eqNSAi+1MgDCHLqxr49dItPL16B7Ut7WRlpPHuyWV8+e+mcsG0co0JiMhBKRCGgFferuMHz63nzxvqyM1M5/wTR3LxyaM478SRmgZaRAZMZ4tjlLvz4oZafvDsBl7bWM+Igmy+/r5pfPSMCk0RISJHRGeOY4y78/zanXz/2Q2s2Bxj1LAcbr/0JK45vULjAiLyjigQjhHuzsI3d/D959azamsTY4tzuePyk/lI5TiyMxQEIvLOKRBSXE+L4N6F63ljayMThudx94dmcsWcsWRqrWARGUQKhBTl7vxpXQ33PrOelZtjjC/N5f99eCZXzB6rReNFJCESEghm9hAwDXjC3e/o5/kS4BGgEFjt7p/t9Vw58JS7z05EbamuZ7D43oXrWFYVY2xxLt/50AyunDNOLQIRSahBDwQzuxJId/ezzew+M5vs7uv7bHYt8At3/6WZPWJmle6+JHzuu0DuYNd1LFi/o5mvP7qK1zbWM7ooh29fcTIfOXU8WRkKAhFJvES0EOYC88PHzwHnAH0DoQ6YambFwHigCsDMzgd2AdUJqCtluTv/t3QLtz22ivysDL552XSuPm28BotF5KhKRCDkA1vDx03ApH62eRF4P3ALsAZoMLMs4DbgcmBBfy9sZjcCNwJUVFQMZs1Js6u9i28sWMVvl2/lrOOH871rTmHkMK02JiJHXyICoYW9XT4FQH/9HXcCn3X3JjP7EvApYBTwI3ePHWjhdXd/EHgQoLKy0vvd6BiyprqJmx5Zxtu1u/jCBZO5+fzJpGvVMRFJkkR0Ti8l6CYCmAVs7GebPGCGmaUDZwAOXADcZGaLgFPM7L8SUFtKcHd+9VoVl/3wzzTt7uKR68/gCxdMURiISFIlooWwAFhsZmOAS4BrzOwOd7+11zZ3AT8FJgAvA/Pc/Sc9T5rZIne/IQG1JV1Lexdff/QNHluxjXMmjeDeq0+hrFArkIlI8g16IITdQHOBC4G73b0aWNlnm9eA6Qd5jbmDXVcqqGtp5yM/fpmNtbv48oVT+Px5k9QqEJGUkZD7ENy9gb1XGgnBGgVf+N8VbGlo4xc3nMHZJ4xIdkkiIvvQBe5HyY+e38Di9bX8+wenKwxEJCUpEI6CP2+o5d5n1nHF7LFcc9r4ZJcjItIvBUKC7WzazT/9ajknlBVwx+Unc6BLakVEkk2T2yVQV3ecm+ctZ1d7N/M+M0cL14hIStMZKoHuWbiOV/9Wz71Xz2JyeWGyyxEROSh1GSXI82t2ct+it/j708dzxexxyS5HROSQFAgJsDXWxhfnr2Da6GH826UHvN1CRCSlKBAGWUdXnH/85TK6up37PjZH6xyLyDFDYwiD7K4n/8ryqhg/+ugcjhuRn+xyREQGTC2EQfTjP73FT/+8kU+ePZH3zxyd7HJERA6LAmGQ/PyVTdz15BounTWGb3zgpGSXIyJy2BQIg+C3y7bwjQWruGDaSO65apYmrBORY5IC4R16atV2/vn/VvKuScP54UfnkJmuQyoixyadvd6BRWt3cvO85cyuKOEn11XqiiIROaYpEI7QK2/X8Q8/X8qU8kIe/uRp5GXpgi0RObYpEI7Ais0xrv/vvzC+NI+fffp0inIzk12SiMg7pkA4TGuqm/jEw68xvCCbR244g+EFWv5SRIYGBcJh+spv3iAnM41HbjiD8mE5yS5HRGTQKBAOw99qd7Fyc4wbzjme8aV5yS5HRGRQKRAOw4LlWzGDS2eNSXYpIiKDToEwQO7OYyu2ctbxwxlVpK4iERl6FAgDtHJLIxvrWrn8lLHJLkVEJCEUCAO0YPlWsjLSuHjGqGSXIiKSEAqEAejqjvP469t474kjGZajew5EZGhSIAzAn9+qo7alg8vUXSQiQ1hCAsHMHjKzl8zs1gM8X2JmT5jZYjN7IPxekZk9aWYLzexRM8tKRG1H4rHlWxmWk8F5J5YluxQRkYQZ9EAwsyuBdHc/GxhjZpP72exa4Bfufi5QaGaVwMeAe9z9QqAauHiwazsSbR3dPL26mvfNGE12hiavE5GhKxEzss0F5oePnwPOAdb32aYOmGpmxcB4oMrdl/R6vgzYmYDaDtvCv+5gV0e3uotEZMhLRJdRPrA1fNwElPezzYvAZOAWYA3Q0POEmZ0FlLj7K313MrMbzWyJmS2pqakZ9ML7s2D5VkYX5XDGcaVH5eeJiCRLIgKhBcgNHxcc4GfcCXzW3b9JEAifAjCzUuAHwKf7e2F3f9DdK929sqws8f359bs6eGFdDR+cNYY0rYImIkNcIgJhKUE3EcAsYGM/2+QBM8wsHTgD8HAQeT7wVXfflIC6DtsfXt9GV9zVXSQikZCIQFgAXGtm9wBXAavN7I4+29wFPAg0AqXAPOB64FTg62a2yMyuTkBth2XBim1MKS9g2ujCZJciIpJwhxxUNrMc4GR3X2Jm1wM/d/eOA23v7k1mNhe4ELjb3auBlX22eQ2Y3mfX+8OPlLC5vpWlmxr4l4umYqbuIhEZ+gbSQpjP3pN3OfDIoXZw9wZ3nx+GwTHpsRXBuPhlp2hmUxGJhoEEQom7/w+Au98JjEhsScnn7ixYsY3TJpYwrkTrHohINAwkELaY2VfM7Dwz+wopcn9AIq3e1sSGnS0aTBaRSBlIIHwSaAU+DOwCrktkQangsRVbyUgz3j9jdLJLERE5agYSCAa87O43AW2AJ7ak5OqOO79buY25U8soyU+Z6ZRERBIuIYPKx7JX365jR1O7uotEJHI0qNzHC+tryUw3LpjW34wbIiJD10Amt9sSDia/BpzGEB9UbtjVQUleFrlZmtlURKLlcAeV2xjig8qxtg6K87QqmohEzyFbCO7ebma/Yu+EdXOAlxNaVRLFWjspztVgsohEz0CmrngIOA4oIWgpOHsnrxtyGts6dTOaiETSQLqMJhCsXrYBeA8QT2hFSdbY1qkuIxGJpIEEQjvwXiAd+AhBS2HICrqMFAgiEj0DCYSrCJbA/CIwDfh8QitKovaubto6u9VCEJFIGsig8i6C7iKA2xJbTnI1tnUCUKQWgohEUCIWyDlmNbaGgZCnq4xEJHoUCL3EwhaCxhBEJIoUCL3EwhaCxhBEJIoUCL1oDEFEomwgN6aNBM4H9nSsu/vPEllUssRag6WidaeyiETRQFoITwHjCNZF6PkYkhrbOjGDwpyBzPknIjK0DOTM1+Tu3014JSkg1tpJUW4maWlDNvNERA5oIIHwopnNA35GsIQm7v5CQqtKksY23aUsItE1kEDoBNYAp4dfOzAkAyHW1qkBZRGJrIHcqfzvR6OQVNDY2qGb0kQksnTZaS8xdRmJSIQdMBDM7J7w8/Nm9lz48byZPXeoFzWzh8zsJTO79QDPl5jZE2a22MweGOh+iaapr0Ukyg4YCO7+pfDzee5+fvhxnruff7AXNLMrgXR3PxsYY2aT+9nsWuAX7n4uUGhmlQPcL2HicadRYwgiEmGJ6DKaC8wPHz9H/6ur1QFTzawYGA9UDXC/hGne3YW77lIWkegaUCCYWZmZVYQfZx1i83xga/i4CSjvZ5sXgcnALQRXMDUMZD8zu9HMlpjZkpqamoGUPmCxtvAuZQ0qi0hEJWJN5RYgN3xcQP+hcyfwWXdvMrMvAZ8ayH7u/iDwIEBlZaUfqvbD0aiZTkUk4hKxpvJS9gbGLGBjP9vkATPMLB04gyBkBrJfwsT2rIWgQBCRaBrIjWmHu6byAmCxmY0BLgGuMbM73L33lUN3AT8lCJuXgXkE4dR7vzMP49/xjmktBBGJuoEEwlXAaII1la/nEGsqh91Ac4ELgbvdvRpY2Web14Dpfffts1/jAGobNI3hTKdqIYhIVCVkTWV3b2DvFUMDdqT7DQathSAiUXfIMQQze/JoFJJssdZOcjPTyc5IT3YpIiJJMZBB5TfM7LKEV5JkMd2lLCIRN5AxhNOAm83sDYLpr/1Qdysfi3rWQhARiaqBjCGcdzQKSbYmtRBEJOIOe+oKMzuqU0ocLbG2Dq2lLCKRNpBB5YV9vnVXgmpJKnUZiUjUHbDLyMxmArOBsWZ2XfjtfGD30SjsaNOgsohE3cFaCNbP5zqCG9WGlN2d3XR0xXVTmohE2gFbCO6+ElhpZlPd/WdHsaajrmceI40hiEiUHXIMwd2/djQKSaaeqa81hiAiUaY1lenVQlCXkYhEmAIBzWMkIgIKBAAa1UIQEVEggMYQRERAgQAEYwjpaUZB9kCmdhIRGZoUCARjCMW5mZjZoTcWERmiFAgEdynrpjQRiToFAsGgssYPRCTqFAj0zHSqQBCRaFMgEAwqF+dp2goRiTYFAsGgsrqMRCTqIh8IXd1xmnd3KRBEJPIiHwhNu7sA3aUsIhL5QIi1BncpKxBEJOoiHwg9E9tpLQQRibqEBIKZPWRmL5nZrQd4/nNmtij8WGFmPzazEjN7wswWm9kDiairP7GemU7VQhCRiBv0QDCzK4F0dz8bGGNmk/tu4+73u/tcd58LLAYeBK4FfuHu5wKFZlY52LX1p2emUw0qi0jUJaKFMBeYHz5+DjjnQBua2Vig3N2XEqzXPNXMioHxQFUCatvPnjEEBYKIRFwiAiEf2Bo+bgLKD7LtTcD94eMXgcnALcAaoKHvxmZ2o5ktMbMlNTU1g1JsY1twlZFaCCISdYkIhBYgN3xccKCfYWZpwHnu/nz4rTuBz7r7NwkC4VN993H3B9290t0ry8rKBqXYWFsHhdkZZKRHfnxdRCIuEWfBpeztJpoFbDzAducCr/b6Og+YYWbpwBmAJ6C2/TS2djJMrQMRkYQEwgLgWjO7B7gKWG1md/Sz3UXAC72+votgcLkRKAXmJaC2/cTaOnUPgogIMOhLhLl7k5nNBS4E7nb3amBlP9t9rc/XrwHTB7ueQ2lUIIiIAAm6D8HdG9x9fhgGKS3W2qGb0kRE0J3KNLZpDEFEBCIeCO4eroWgQBARiXQgtHZ00xV33ZQmIkLEA6FnHiO1EEREoh4I4bQVuktZRCTigbB3YjtdZSQiEu1AUJeRiMgekQ4EjSGIiOwV7UBo1WppIiI9oh0IbR1kpaeRkxnpwyAiAkQ8EJraOinKy8TMkl2KiEjSRToQYq2duilNRCSkQNCAsogIEPVAaOvUTWkiIqFIB0JTW6duShMRCUU6EGKtHeoyEhEJRTYQOrri7Oro1qCyiEgosoHQM21FkVoIIiKAAkGDyiIioQgHQjD1dXGeBpVFRCDCgbB3HiO1EEREQIGgLiMRkVBkA0FrIYiI7CuygRBr68QMCnMUCCIiEOFAaGztYFhOJulpmulURAQiHAiax0hEZF8JCQQze8jMXjKzWw/w/OfMbFH4scLMftzrufvM7NJE1NVbY5tmOhUR6W3QA8HMrgTS3f1sYIyZTe67jbvf7+5z3X0usBh4MNz3XGCUu/9+sOvqK9aqFoKISG+JaCHMBeaHj58DzjnQhmY2Fih396Vmlgn8BNhoZpcdYPsbzWyJmS2pqal5R0UGLQTdlCYi0iMRgZAPbA0fNwHlB9n2JuD+8PF1wJvA3cDpZnZz343d/UF3r3T3yrKysndUZKy1QzeliYj0kohAaAFyw8cFB/oZZpYGnOfuz4ffmg086O7VwC+A8xJQGwDxuNOoQWURkX0kIhCWsrebaBaw8QDbnQu82uvrDcDx4eNKYFMCagOgpaOLuOumNBGR3jIS8JoLgMVmNga4BLjGzO5w975XHF0EvNDr64eAh83sGiAT+HACagOgUdNWiIjsZ9ADwd2bzGwucCFwd9gFtLKf7b7W5+tm4CODXU9/9kxsp0FlEZE9EtFCwN0b2HulUcqJhVNfq4UgIrJXJO9U1sR2IiL7i2QgaC0EEZH9RTIQeloIwxQIIiJ7RDIQYq0d5GSmkZOZnuxSRERSRiQDobGtk+JcXWEkItJbJAMh1qqZTkVE+opmIGjaChGR/UQyEBo19bWIyH6iGQhaHEdEZD+RDIRYW4emrRAR6SNygbC7s5vdnXF1GYmI9BG5QNC0FSIi/YtsIKiFICKyr8gFwt55jDSGICLSWwQDIZj6Wl1GIiL7ilwglOZnccnJoxhZmJ3sUkREUkpCFshJZZUTS6mcWJrsMkREUk7kWggiItI/BYKIiAAKBBERCSkQREQEUCCIiEhIgSAiIoACQUREQgoEEREBwNw92TUcETOrATYd4e4jgNpBLGcwqbYjk8q1QWrXp9qOzLFa2wR3L+vviWM2EN4JM1vi7pXJrqM/qu3IpHJtkNr1qbYjMxRrU5eRiIgACgQREQlFNRAeTHYBB6Hajkwq1wapXZ9qOzJDrrZIjiGIiMj+otpCEBE5LGZWamYXmtmIZNeSKAqEFGFmGWZWZWaLwo8Zya4p1ZlZuZktDh+PNbMtvY5fv5fVRZ2ZFZnZk2a20MweNbOsVPq9S9WTrpmNBv4AnA48b2ZlqXTcBkvkuozM7CFgGvCEu9+R7Hp6mNkc4Gp3/0qya+nNzMqBX7v7uWaWCTwKlAL/5e4PJ7GuEmAeMNLd55jZlUC5u9+frJp6mFkR8CuCBahagKuB+0mB3zsz+zyw3t0Xmtn9wHYgPxV+78KT7m+Bx4FrgPOB/yA1jtsFQIu7v2Jm3wVqgNJUOG49wr/Vp9x99pGe5yLVQghPGunufjYwxswmJ7umXs4ErjCzF83sETNL+mp24Un3f4D88Fs3A0vC4/cBMytMWnHQTXCibQq/PhP4vJm9bGb3Jq8sAD4G3OPuFwLVBCe3lPi9c/f73H1h+GUZ0EXq/N5NB77o7t8GniYIhFQ5bs+EYfBuglZCG6lz3Hp8F8h9J+e5SAUCMBeYHz5+DjgneaXs5y/Ae9z9HCAGvC+55QD7n3Tnsvf4vQQk7aYcd29y98Ze33oSONvdzwKmmNnMJJXW30n346TY752ZnQWUAAtJkd+7fk66F5FCx83MjODvoRNYSYocNwAzOx/YRfAGZC5HeNyiFgj5wNbwcRNQnsRa+nrd3beHj9cASW+99HPSTeXj95K7N4ePU+L49TrpbiaFjpuZlQI/AD5Niv3e9TnpGil03DxwE8GboVGpctzMLAu4DfjX8FtH/HcatUBoAXLDxwWk1r//52Y2y8zSgSsI3oGkmlQ+fk+b2WgzyyN4Z7kqmcX0OemmzHELTx7zga+6+yZS7Peuz0n3TFLnuH3FzK4LvywGHkih4/avwI/cPRZ+fcS/b6n0B300LGVv82kWsDF5peznm8DPgRXAy+7+THLL6VcqH79/B54HXgEecPe1ySqkn5NuKh2364FTga+b2SJgNSnye9fPSfc/SJ3j9iBwrZm9AKQD7yZFjhtwAXBT+P95CnApR3jcInWVkZkNAxYDzwKXAGf26RKRfpjZInefa2YTgCeAZ4CzCY5fd3KrSz1m9jngTva+a/wp8CX0e3dQ4UUM84FsghbeV4EX0HEbsDAUPsgRnuciFQiw55fuQuAFd69Odj3HGjMbQ/Du42n9cQ6cfu+OjI7bkTnS4xa5QBARkf5FbQxBREQOQIEgIiKAAkHkqDKzT5rZJ5Ndh0h/FAgiIgIEk2+JyEGEN7v9DBgJvEEwsdkZQF74+Bp37zKzHxBcBx4Drgs//zD8XifBnEYAs8zsOWAUcJW7J/UmOpEeaiGIHNqNwCp3fzcwGpgJLHb39wA7gMvM7ANAjrufC/wa+ArBDUIZ7v4ugonHTg1f7zSCu6lvJ7hmXCQlKBBEDm0qwcyWi4DjgbEEdx8DvA5MBE4CXg2/9yrB1MMnAq8BuPvjBBPwAcxz905gJ5CV+PJFBkaBIHJoa4H/dPe5wK1AFcFsnACzgQ0EU0CcGX7vzPDrNQStAczsY8C3wud3HZWqRQ6TxhBEDu0nwE/N7FMEs0euA04LWwzVwO/dPW5mF4cruMXYO4ZwSTj/TStwLfD+o1++yMDoTmWRw2RmtwOL3H1RkksRGVQKBBERATSGICIiIQWCiIgACgQREQkpEEREBFAgiIhISIEgIiIA/H82vVvM6BhMOgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 解决中文乱码\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "\n",
    "plt.plot(range(epochs), test_acc)\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"test acc\")\n",
    "plt.title(\"测试集准确度\")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(range(epochs), train_loss)\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"test loss\")\n",
    "plt.title(\"训练集损失\")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(range(epochs), train_acc)\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"train acc\")\n",
    "plt.title(\"训练集准确度\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实验结果分析\n",
    "\n",
    "据可视化的实验结果，模型在训练集上的损失逐渐降低，准确度逐渐升高。虽然模型在测试集的准确度曲线有明显波动，中途会有下降，但总体还是呈上升趋势，最终趋于稳定。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "learn",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
